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diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..0e87a953bcd8ba1c01126907b6085686e2d5d135
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,19 @@
+# Secrets — never commit
+.env
+.env.*
+!.env.example
+
+# Python
+__pycache__/
+*.py[cod]
+.Python
+*.egg-info/
+.venv/
+venv/
+
+# IDE / local
+.cursor/
+*.swp
+
+# OS
+.DS_Store
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..5279e99ba5fec85ed7a0cd53df76c6b9cf2cdf07
--- /dev/null
+++ b/README.md
@@ -0,0 +1,103 @@
+# Learnable Telegraph Diffusion for Image Denoising
+
+This repository contains stage-wise trained denoising models for additive Gaussian noise removal, together with a TNRD-style baseline and a classical PDE baseline.
+
+The results below summarize the latest complete overnight sweep:
+
+- log directory: `logs/overnight_20260408_013608`
+- evaluated datasets: `Set12`, `BSD68`
+- noise levels: `sigma = 15, 25, 50, 75`
+- stage count: `5`
+
+Note: the earlier run in `logs/overnight_20260408_013234` was a scheduler-bug run and is not used for the summary below.
+
+## Main Takeaways
+
+- The strongest model in this sweep is the `Finetuned TNRD baseline` on both `Set12` and `BSD68` at every tested noise level.
+- For both MLP and RBF parameterizations, the `No-wave` variant outperformed the `Telegraph` variant throughout this sweep.
+- The `RBF` parameterization consistently outperformed the corresponding `MLP` parameterization.
+- End-to-end fine-tuning improved every model family over its stage-wise checkpoint.
+- All learned models clearly outperformed the classical PDE baseline at the tested noise levels.
+
+## Best Results
+
+| Dataset | Sigma | Best model | PSNR (dB) |
+|---|---:|---|---:|
+| BSD68 | 15 | Finetuned TNRD baseline | 30.90 |
+| BSD68 | 25 | Finetuned TNRD baseline | 28.36 |
+| BSD68 | 50 | Finetuned TNRD baseline | 25.43 |
+| BSD68 | 75 | Finetuned TNRD baseline | 23.91 |
+| Set12 | 15 | Finetuned TNRD baseline | 31.85 |
+| Set12 | 25 | Finetuned TNRD baseline | 29.33 |
+| Set12 | 50 | Finetuned TNRD baseline | 26.05 |
+| Set12 | 75 | Finetuned TNRD baseline | 24.18 |
+
+## Plots
+
+### Base Models
+
+![Base model sweep](plots/base_model_sweep.png)
+
+### Finetuned Models
+
+![Finetuned model sweep](plots/finetuned_model_sweep.png)
+
+## BSD68 Results
+
+### Base Models
+
+| Method | 15 | 25 | 50 | 75 |
+|---|---:|---:|---:|---:|
+| MLP Telegraph | 28.31 | 25.06 | 22.86 | 20.88 |
+| MLP No-wave | 29.08 | 26.87 | 23.87 | 21.71 |
+| RBF Telegraph | 27.97 | 25.52 | 22.70 | 20.89 |
+| RBF No-wave | 30.46 | 27.86 | 24.47 | 22.30 |
+| TNRD baseline | 30.41 | 27.85 | 24.58 | 22.42 |
+
+### Finetuned Models
+
+| Method | 15 | 25 | 50 | 75 |
+|---|---:|---:|---:|---:|
+| Finetuned MLP Telegraph | 29.90 | 27.30 | 24.42 | 22.40 |
+| Finetuned MLP No-wave | 29.88 | 27.61 | 24.60 | 22.93 |
+| Finetuned RBF Telegraph | 30.56 | 27.70 | 24.65 | 23.26 |
+| Finetuned RBF No-wave | 30.79 | 28.30 | 25.23 | 23.74 |
+| Finetuned TNRD baseline | 30.90 | 28.36 | 25.43 | 23.91 |
+
+## Set12 Results
+
+### Base Models
+
+| Method | 15 | 25 | 50 | 75 |
+|---|---:|---:|---:|---:|
+| MLP Telegraph | 29.19 | 25.92 | 23.32 | 20.99 |
+| MLP No-wave | 29.59 | 27.47 | 24.54 | 22.28 |
+| RBF Telegraph | 29.19 | 26.50 | 23.32 | 21.36 |
+| RBF No-wave | 31.45 | 28.97 | 25.43 | 23.12 |
+| TNRD baseline | 31.43 | 28.94 | 25.54 | 23.20 |
+
+### Finetuned Models
+
+| Method | 15 | 25 | 50 | 75 |
+|---|---:|---:|---:|---:|
+| Finetuned MLP Telegraph | 30.63 | 28.03 | 24.76 | 22.38 |
+| Finetuned MLP No-wave | 30.64 | 28.40 | 25.10 | 23.12 |
+| Finetuned RBF Telegraph | 31.51 | 28.66 | 25.23 | 23.51 |
+| Finetuned RBF No-wave | 31.74 | 29.23 | 25.92 | 24.05 |
+| Finetuned TNRD baseline | 31.85 | 29.33 | 26.05 | 24.18 |
+
+## Classical PDE Baseline
+
+The latest overnight run evaluated the classical PDE baseline at `sigma = 15, 50, 75`.
+
+| Dataset | 15 | 25 | 50 | 75 |
+|---|---:|---:|---:|---:|
+| BSD68 | 26.00 | - | 19.56 | 15.08 |
+| Set12 | 26.73 | - | 19.55 | 14.98 |
+
+## Notes
+
+- The learned models were trained stage-wise first, then optionally fine-tuned end-to-end on the same noise level.
+- Fine-tuned checkpoints and base checkpoints were both evaluated using the same sigma-specific setup.
+- `evaluate_checkpoints.py` produced the main result table used here.
+- `plot_experiment_results.py` generated the plots in `plots/`.
diff --git a/baseline_robustness_curve.png b/baseline_robustness_curve.png
new file mode 100644
index 0000000000000000000000000000000000000000..137b20835bec696fba48aeeed456d30ded6d6364
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+version https://git-lfs.github.com/spec/v1
+oid sha256:8188d201d47ae906f08c7db57d75c44376362908b81acfa1277a6616b8555d0e
+size 190771
diff --git a/classical_baseline.py b/classical_baseline.py
new file mode 100644
index 0000000000000000000000000000000000000000..d15cec91320b685bb7898cbe03fbe62a9c700bf6
--- /dev/null
+++ b/classical_baseline.py
@@ -0,0 +1,116 @@
+import os
+import glob
+import torch
+import torch.nn.functional as F
+import math
+from PIL import Image
+from torchvision import transforms
+from torch.utils.data import Dataset, DataLoader
+
+# --- Configuration ---
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+_SIGMAS = (15.0 / 255.0, 50.0 / 255.0, 75.0 / 255.0)
+
+
+def _testset_root(name):
+    return os.path.join(
+        _SCRIPT_DIR,
+        "datasets",
+        "Test_Datasets",
+        "FFDNet-master",
+        "testsets",
+        name,
+    )
+
+
+class TestDataset(Dataset):
+    def __init__(self, root_dir, sigma):
+        self.sigma = sigma
+        self.image_paths = glob.glob(os.path.join(root_dir, "*.png")) + glob.glob(
+            os.path.join(root_dir, "*.jpg")
+        )
+        self.transform = transforms.Compose(
+            [transforms.Grayscale(num_output_channels=1), transforms.ToTensor()]
+        )
+
+    def __len__(self):
+        return len(self.image_paths)
+
+    def __getitem__(self, idx):
+        img = Image.open(self.image_paths[idx])
+        clean = self.transform(img)
+        noisy = clean + torch.randn_like(clean) * self.sigma
+        return clean, torch.clamp(noisy, 0.0, 1.0)
+
+
+def calculate_psnr(img1, img2):
+    mse = torch.mean((img1 - img2) ** 2)
+    if mse == 0:
+        return float("inf")
+    return 20 * math.log10(1.0 / math.sqrt(mse))
+
+
+def classical_telegraph_step(u_n, u_n_minus_1, tau=0.2, gamma=1.0):
+    kx = torch.tensor([[0, 0, 0], [-0.5, 0, 0.5], [0, 0, 0]], device=DEVICE).view(
+        1, 1, 3, 3
+    )
+    ky = torch.tensor([[0, -0.5, 0], [0, 0, 0], [0, 0.5, 0]], device=DEVICE).view(
+        1, 1, 3, 3
+    )
+
+    grad_x = F.conv2d(u_n, kx, padding=1)
+    grad_y = F.conv2d(u_n, ky, padding=1)
+    grad_mag = torch.sqrt(grad_x**2 + grad_y**2 + 1e-8)
+
+    c = 1.0 / (1.0 + (grad_mag / 0.1) ** 2)
+
+    divergence = F.conv2d(c * grad_x, kx, padding=1) + F.conv2d(
+        c * grad_y, ky, padding=1
+    )
+
+    alpha = (2 + gamma * tau) / (1 + gamma * tau)
+    beta = -1 / (1 + gamma * tau)
+    lam = (tau**2) / (1 + gamma * tau)
+
+    return alpha * u_n + beta * u_n_minus_1 + lam * divergence
+
+
+def run_eval(dataset_name, sigma):
+    root = _testset_root(dataset_name)
+    dataset = TestDataset(root, sigma)
+    if len(dataset) == 0:
+        print(f"[!] Skip {dataset_name}: no images in {os.path.abspath(root)}")
+        return None
+
+    dataloader = DataLoader(dataset, batch_size=1, shuffle=False)
+    total_psnr = 0.0
+    for clean, noisy in dataloader:
+        clean, noisy = clean.to(DEVICE), noisy.to(DEVICE)
+        u_n_minus_1, u_n = noisy.clone(), noisy.clone()
+
+        for _ in range(20):
+            u_next = classical_telegraph_step(u_n, u_n_minus_1)
+            u_n_minus_1, u_n = u_n, u_next
+
+        total_psnr += calculate_psnr(clean, u_n)
+
+    avg_psnr = total_psnr / len(dataset)
+    sigma_int = int(round(sigma * 255.0))
+    print(
+        f"[+] {dataset_name}  sigma={sigma_int}/255  PSNR: {avg_psnr:.2f} dB  "
+        f"({len(dataset)} images)"
+    )
+    return avg_psnr
+
+
+def main():
+    print("[*] Classical Majee 2020 baseline — Set12 & BSD68")
+    for dataset_name in ("Set12", "BSD68"):
+        for sigma in _SIGMAS:
+            run_eval(dataset_name, sigma)
+        print()
+
+
+if __name__ == "__main__":
+    main()
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diff --git a/datasets/Test_Datasets/FFDNet-master/Demo_AWGN_Color.m b/datasets/Test_Datasets/FFDNet-master/Demo_AWGN_Color.m
new file mode 100644
index 0000000000000000000000000000000000000000..94ecfa5898884fb8966327c556f7cf975f7bd22b
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/Demo_AWGN_Color.m
@@ -0,0 +1,132 @@
+% This is the testing demo of FFDNet for denoising noisy color images corrupted by
+% AWGN.
+%
+% To run the code, you should install Matconvnet first. Alternatively, you can use the
+% function `vl_ffdnet_matlab` to perform denoising without Matconvnet.
+%
+% "FFDNet: Toward a Fast and Flexible Solution for CNN based Image
+% Denoising" 2018/03/23
+% If you have any question, please feel free to contact with me.
+% Kai Zhang (e-mail: cskaizhang@gmail.com)
+
+% clear; clc;
+
+format compact;
+global sigmas; % input noise level or input noise level map
+addpath(fullfile('utilities'));
+
+folderModel = 'models';
+folderTest  = 'testsets';
+folderResult= 'results';
+imageSets   = {'CBSD68','Kodak24','McMaster'}; % testing datasets
+setTestCur  = imageSets{1};      % current testing dataset
+
+showResult  = 1;
+useGPU      = 1;
+pauseTime   = 0;
+
+imageNoiseSigma = 25;  % image noise level
+inputNoiseSigma = 25;  % input noise level
+
+folderResultCur       =  fullfile(folderResult, [setTestCur,'_',num2str(imageNoiseSigma(1)),'_',num2str(inputNoiseSigma(1))]);
+if ~isdir(folderResultCur)
+    mkdir(folderResultCur)
+end
+
+load(fullfile('models','FFDNet_color.mat'));
+net = vl_simplenn_tidy(net);
+
+% for i = 1:size(net.layers,2)
+%     net.layers{i}.precious = 1;
+% end
+
+if useGPU
+    net = vl_simplenn_move(net, 'gpu') ;
+end
+
+% read images
+ext         =  {'*.jpg','*.png','*.bmp','*.tif'};
+filePaths   =  [];
+for i = 1 : length(ext)
+    filePaths = cat(1,filePaths, dir(fullfile(folderTest,setTestCur,ext{i})));
+end
+
+% PSNR and SSIM
+PSNRs = zeros(1,length(filePaths));
+SSIMs = zeros(1,length(filePaths));
+
+for i = 1:length(filePaths)
+    
+    % read images
+    label   = imread(fullfile(folderTest,setTestCur,filePaths(i).name));
+    [w,h,c] = size(label);
+    
+    if c == 3
+        [~,nameCur,extCur] = fileparts(filePaths(i).name);
+        label = im2double(label);
+        
+        % add noise
+        randn('seed',0);
+        noise = bsxfun(@times,randn(size(label)),permute(imageNoiseSigma/255,[3 4 1 2]));
+        input = single(label + noise);
+        
+        if mod(w,2)==1
+            input = cat(1,input, input(end,:,:)) ;
+        end
+        if mod(h,2)==1
+            input = cat(2,input, input(:,end,:)) ;
+        end
+        
+        % tic;
+        if useGPU
+            input = gpuArray(input);
+        end
+        
+        % set noise level map
+        sigmas = inputNoiseSigma/255; % see "vl_simplenn.m".
+        
+        % perform denoising
+        res    = vl_simplenn(net,input,[],[],'conserveMemory',true,'mode','test'); % matconvnet default
+        % res    = vl_ffdnet_concise(net, input);    % concise version of vl_simplenn for testing FFDNet
+        % res    = vl_ffdnet_matlab(net, input); % use this if you did  not install matconvnet; very slow
+        
+        % output = input -res(end).x; % for 'model_color.mat'
+        output = res(end).x;
+        
+        
+        if mod(w,2)==1
+            output = output(1:end-1,:,:);
+            input  = input(1:end-1,:,:);
+        end
+        if mod(h,2)==1
+            output = output(:,1:end-1,:);
+            input  = input(:,1:end-1,:);
+        end
+        
+        if useGPU
+            output = gather(output);
+            input  = gather(input);
+        end
+        %toc;
+        
+        % calculate PSNR, SSIM and save results
+        [PSNRCur, SSIMCur] = Cal_PSNRSSIM(im2uint8(label),im2uint8(output),0,0);
+        if showResult
+            imshow(cat(2,im2uint8(input),im2uint8(label),im2uint8(output)));
+            title([filePaths(i).name,'    ',num2str(PSNRCur,'%2.2f'),'dB','    ',num2str(SSIMCur,'%2.4f')])
+            %imwrite(im2uint8(output), fullfile(folderResultCur, [nameCur, '_' num2str(imageNoiseSigma(1),'%02d'),'_' num2str(inputNoiseSigma(1),'%02d'),'_PSNR_',num2str(PSNRCur*100,'%4.0f'), extCur] ));
+            drawnow;
+            pause()
+        end
+        disp([filePaths(i).name,'    ',num2str(PSNRCur,'%2.2f'),'dB','    ',num2str(SSIMCur,'%2.4f')])
+        PSNRs(i) = PSNRCur;
+        SSIMs(i) = SSIMCur;
+        
+    end
+end
+
+disp([mean(PSNRs),mean(SSIMs)]);
+
+
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/Demo_AWGN_Color_Clip.m b/datasets/Test_Datasets/FFDNet-master/Demo_AWGN_Color_Clip.m
new file mode 100644
index 0000000000000000000000000000000000000000..22f9a62e6245e62612445ab9f0926324cea89be0
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/Demo_AWGN_Color_Clip.m
@@ -0,0 +1,132 @@
+% This is the testing demo of FFDNet for denoising noisy color images corrupted by
+% AWGN with clipping setting. The noisy input is 8-bit quantized.
+%
+% To run the code, you should install Matconvnet first. Alternatively, you can use the
+% function `vl_ffdnet_matlab` to perform denoising without Matconvnet.
+%
+% "FFDNet: Toward a Fast and Flexible Solution for CNN based Image
+% Denoising" 2018/03/23
+% If you have any question, please feel free to contact with me.
+% Kai Zhang (e-mail: cskaizhang@gmail.com)
+
+% clear; clc;
+
+format compact;
+global sigmas; % input noise level or input noise level map
+addpath(fullfile('utilities'));
+
+folderModel = 'models';
+folderTest  = 'testsets';
+folderResult= 'results';
+imageSets   = {'CBSD68','Kodak24','McMaster'}; % testing datasets
+setTestCur  = imageSets{1};      % current testing dataset
+
+showResult  = 1;
+useGPU      = 1;
+pauseTime   = 0;
+
+imageNoiseSigma = 25;  % image noise level, 25.5 is the default setting of imnoise( ,'gaussian')
+inputNoiseSigma = 25;  % input noise level
+
+folderResultCur       =  fullfile(folderResult, [setTestCur,'_Clip_',num2str(imageNoiseSigma(1)),'_',num2str(inputNoiseSigma(1))]);
+if ~isdir(folderResultCur)
+    mkdir(folderResultCur)
+end
+
+load(fullfile('models','FFDNet_Clip_color.mat'));
+net = vl_simplenn_tidy(net);
+
+% for i = 1:size(net.layers,2)
+%     net.layers{i}.precious = 1;
+% end
+
+if useGPU
+    net = vl_simplenn_move(net, 'gpu') ;
+end
+
+% read images
+ext         =  {'*.jpg','*.png','*.bmp','*.tif'};
+filePaths   =  [];
+for i = 1 : length(ext)
+    filePaths = cat(1,filePaths, dir(fullfile(folderTest,setTestCur,ext{i})));
+end
+
+% PSNR and SSIM
+PSNRs = zeros(1,length(filePaths));
+SSIMs = zeros(1,length(filePaths));
+
+for i = 1:length(filePaths)
+    
+    % read images
+    label   = imread(fullfile(folderTest,setTestCur,filePaths(i).name));
+    [w,h,c] = size(label);
+    
+    if c == 3
+        [~,nameCur,extCur] = fileparts(filePaths(i).name);
+        label = im2single(label);
+        
+        % add noise
+        randn('seed',0);
+        %input = imnoise(label,'gaussian'); % corresponds to imageNoiseSigma = 25.5;
+        input = imnoise(label,'gaussian',0,(imageNoiseSigma/255)^2);
+        
+        if mod(w,2)==1
+            input = cat(1,input, input(end,:,:)) ;
+        end
+        if mod(h,2)==1
+            input = cat(2,input, input(:,end,:)) ;
+        end
+        
+        % tic;
+        if useGPU
+            input = gpuArray(input);
+        end
+        
+        % set noise level map
+        sigmas = inputNoiseSigma/255; % see "vl_simplenn.m".
+        
+        % perform denoising
+        res    = vl_simplenn(net,input,[],[],'conserveMemory',true,'mode','test'); % matconvnet default
+        % res    = vl_ffdnet_concise(net, input);    % concise version of vl_simplenn for testing FFDNet
+        % res    = vl_ffdnet_matlab(net, input); % use this if you did  not install matconvnet; very slow
+        
+        % output = input -res(end).x; % for 'model_color.mat'
+        output = res(end).x;
+        
+        
+        if mod(w,2)==1
+            output = output(1:end-1,:,:);
+            input  = input(1:end-1,:,:);
+        end
+        if mod(h,2)==1
+            output = output(:,1:end-1,:);
+            input  = input(:,1:end-1,:);
+        end
+        
+        if useGPU
+            output = gather(output);
+            input  = gather(input);
+        end
+        %toc;
+        
+        % calculate PSNR, SSIM and save results
+        [PSNRCur, SSIMCur] = Cal_PSNRSSIM(im2uint8(label),im2uint8(output),0,0);
+        if showResult
+            imshow(cat(2,im2uint8(input),im2uint8(label),im2uint8(output)));
+            title([filePaths(i).name,'    ',num2str(PSNRCur,'%2.2f'),'dB','    ',num2str(SSIMCur,'%2.4f')])
+            %imwrite(im2uint8(output), fullfile(folderResultCur, [nameCur, '_' num2str(imageNoiseSigma(1),'%02d'),'_' num2str(inputNoiseSigma(1),'%02d'),'_PSNR_',num2str(PSNRCur*100,'%4.0f'), extCur] ));
+            drawnow;
+            pause()
+        end
+        disp([filePaths(i).name,'    ',num2str(PSNRCur,'%2.2f'),'dB','    ',num2str(SSIMCur,'%2.4f')])
+        PSNRs(i) = PSNRCur;
+        SSIMs(i) = SSIMCur;
+        
+    end
+end
+
+disp([mean(PSNRs),mean(SSIMs)]);
+
+
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/Demo_AWGN_Gray.m b/datasets/Test_Datasets/FFDNet-master/Demo_AWGN_Gray.m
new file mode 100644
index 0000000000000000000000000000000000000000..c80c5879c5089b318f637c9221e528e433ef112b
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/Demo_AWGN_Gray.m
@@ -0,0 +1,131 @@
+% This is the testing demo of FFDNet for denoising noisy grayscale images corrupted by
+% AWGN.
+%
+% To run the code, you should install Matconvnet first. Alternatively, you can use the
+% function `vl_ffdnet_matlab` to perform denoising without Matconvnet.
+%
+% "FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising"
+%  2018/03/23
+% If you have any question, please feel free to contact with me.
+% Kai Zhang (e-mail: cskaizhang@gmail.com)
+
+%clear; clc;
+format compact;
+global sigmas; % input noise level or input noise level map
+addpath(fullfile('utilities'));
+
+folderModel = 'models';
+folderTest  = 'testsets';
+folderResult= 'results';
+imageSets   = {'BSD68','Set12'}; % testing datasets
+setTestCur  = imageSets{1};      % current testing dataset
+
+showResult  = 1;
+useGPU      = 1; % CPU or GPU. For single-threaded (ST) CPU computation, use "matlab -singleCompThread" to start matlab.
+pauseTime   = 0;
+
+imageNoiseSigma = 50;  % image noise level
+inputNoiseSigma = 50;  % input noise level
+
+folderResultCur       =  fullfile(folderResult, [setTestCur,'_',num2str(imageNoiseSigma),'_',num2str(inputNoiseSigma)]);
+if ~isdir(folderResultCur)
+    mkdir(folderResultCur)
+end
+
+load(fullfile('models','FFDNet_gray.mat'));
+net = vl_simplenn_tidy(net);
+
+% for i = 1:size(net.layers,2)
+%     net.layers{i}.precious = 1;
+% end
+
+if useGPU
+    net = vl_simplenn_move(net, 'gpu') ;
+end
+
+% read images
+ext         =  {'*.jpg','*.png','*.bmp'};
+filePaths   =  [];
+for i = 1 : length(ext)
+    filePaths = cat(1,filePaths, dir(fullfile(folderTest,setTestCur,ext{i})));
+end
+
+% PSNR and SSIM
+PSNRs = zeros(1,length(filePaths));
+SSIMs = zeros(1,length(filePaths));
+
+for i = 1:length(filePaths)
+    
+    % read images
+    label = imread(fullfile(folderTest,setTestCur,filePaths(i).name));
+    [w,h,~]=size(label);
+    if size(label,3)==3
+        label = rgb2gray(label);
+    end
+    
+    [~,nameCur,extCur] = fileparts(filePaths(i).name);
+    label = im2double(label);
+    
+    % add noise
+    randn('seed',0);
+    noise = imageNoiseSigma/255.*randn(size(label));
+    input = single(label + noise);
+    
+    if mod(w,2)==1
+        input = cat(1,input, input(end,:)) ;
+    end
+    if mod(h,2)==1
+        input = cat(2,input, input(:,end)) ;
+    end
+    
+    % tic;
+    if useGPU
+        input = gpuArray(input);
+    end
+    
+    % set noise level map
+    sigmas = inputNoiseSigma/255; % see "vl_simplenn.m".
+    
+    % perform denoising
+    res    = vl_simplenn(net,input,[],[],'conserveMemory',true,'mode','test'); % matconvnet default
+    % res    = vl_ffdnet_concise(net, input);    % concise version of vl_simplenn for testing FFDNet
+    % res    = vl_ffdnet_matlab(net, input); % use this if you did  not install matconvnet; very slow
+    
+    
+    % output = input - res(end).x; % for 'model_gray.mat'
+    output = res(end).x;
+    
+    
+    if mod(w,2)==1
+        output = output(1:end-1,:);
+        input  = input(1:end-1,:);
+    end
+    if mod(h,2)==1
+        output = output(:,1:end-1);
+        input  = input(:,1:end-1);
+    end
+    
+    if useGPU
+        output = gather(output);
+        input  = gather(input);
+    end
+    % toc;
+    % calculate PSNR, SSIM and save results
+    [PSNRCur, SSIMCur] = Cal_PSNRSSIM(im2uint8(label),im2uint8(output),0,0);
+    if showResult
+        imshow(cat(2,im2uint8(input),im2uint8(label),im2uint8(output)));
+        title([filePaths(i).name,'    ',num2str(PSNRCur,'%2.2f'),'dB','    ',num2str(SSIMCur,'%2.4f')])
+        %imwrite(im2uint8(output), fullfile(folderResultCur, [nameCur, '_' num2str(imageNoiseSigma,'%02d'),'_' num2str(inputNoiseSigma,'%02d'),'_PSNR_',num2str(PSNRCur*100,'%4.0f'), extCur] ));
+        drawnow;
+        pause(pauseTime)
+    end
+    disp([filePaths(i).name,'    ',num2str(PSNRCur,'%2.2f'),'dB','    ',num2str(SSIMCur,'%2.4f')])
+    PSNRs(i) = PSNRCur;
+    SSIMs(i) = SSIMCur;
+end
+
+disp([mean(PSNRs),mean(SSIMs)]);
+
+
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/Demo_AWGN_Gray_Clip.m b/datasets/Test_Datasets/FFDNet-master/Demo_AWGN_Gray_Clip.m
new file mode 100644
index 0000000000000000000000000000000000000000..a326eb85ba63966068f229a6c439ca84afc9f0f7
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/Demo_AWGN_Gray_Clip.m
@@ -0,0 +1,122 @@
+% This is the testing demo of FFDNet for denoising noisy grayscale images corrupted by
+% AWGN with clipping setting. The noisy input is 8-bit quantized.
+% "FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising"
+%  2018/03/23
+% If you have any question, please feel free to contact with me.
+% Kai Zhang (e-mail: cskaizhang@gmail.com)
+
+%clear; clc;
+format compact;
+global sigmas; % input noise level or input noise level map
+addpath(fullfile('utilities'));
+
+folderModel = 'models';
+folderTest  = 'testsets';
+folderResult= 'results';
+imageSets   = {'BSD68','Set12'}; % testing datasets
+setTestCur  = imageSets{1};      % current testing dataset
+
+showResult  = 1;
+useGPU      = 1; % CPU or GPU. For single-threaded (ST) CPU computation, use "matlab -singleCompThread" to start matlab.
+pauseTime   = 0;
+
+imageNoiseSigma = 25;  % image noise level, 25.5 is the default setting of imnoise( ,'gaussian')
+inputNoiseSigma = 25;  % input noise level
+
+folderResultCur       =  fullfile(folderResult, [setTestCur,'_Clip_',num2str(imageNoiseSigma),'_',num2str(inputNoiseSigma)]);
+if ~isdir(folderResultCur)
+    mkdir(folderResultCur)
+end
+
+load(fullfile('models','FFDNet_Clip_gray.mat'));
+net = vl_simplenn_tidy(net);
+
+% for i = 1:size(net.layers,2)
+%     net.layers{i}.precious = 1;
+% end
+
+if useGPU
+    net = vl_simplenn_move(net, 'gpu') ;
+end
+
+% read images
+ext         =  {'*.jpg','*.png','*.bmp'};
+filePaths   =  [];
+for i = 1 : length(ext)
+    filePaths = cat(1,filePaths, dir(fullfile(folderTest,setTestCur,ext{i})));
+end
+
+% PSNR and SSIM
+PSNRs = zeros(1,length(filePaths));
+SSIMs = zeros(1,length(filePaths));
+
+for i = 1:length(filePaths)
+    
+    % read images
+    label = imread(fullfile(folderTest,setTestCur,filePaths(i).name));
+    [w,h,~]=size(label);
+    if size(label,3)==3
+        label = rgb2gray(label);
+    end
+    
+    [~,nameCur,extCur] = fileparts(filePaths(i).name);
+    label = im2single(label);
+    
+    % add noise
+    randn('seed',0);
+    %input = imnoise(label,'gaussian'); % corresponds to imageNoiseSigma = 25.5;
+    input = imnoise(label,'gaussian',0,(imageNoiseSigma/255)^2);
+    
+    if mod(w,2)==1
+        input = cat(1,input, input(end,:)) ;
+    end
+    if mod(h,2)==1
+        input = cat(2,input, input(:,end)) ;
+    end
+    
+    % tic;
+    if useGPU
+        input = gpuArray(input);
+    end
+    
+    % set noise level map
+    sigmas = inputNoiseSigma/255; % see "vl_simplenn.m".
+    
+    % denoising
+    res    = vl_simplenn(net,input,[],[],'conserveMemory',true,'mode','test');
+    output = res(end).x;
+    
+    
+    if mod(w,2)==1
+        output = output(1:end-1,:);
+        input  = input(1:end-1,:);
+    end
+    if mod(h,2)==1
+        output = output(:,1:end-1);
+        input  = input(:,1:end-1);
+    end
+    
+    if useGPU
+        output = gather(output);
+        input  = gather(input);
+    end
+    % toc;
+    % calculate PSNR, SSIM and save results
+    [PSNRCur, SSIMCur] = Cal_PSNRSSIM(im2uint8(label),im2uint8(output),0,0);
+    if showResult
+        imshow(cat(2,im2uint8(input),im2uint8(label),im2uint8(output)));
+        title([filePaths(i).name,'    ',num2str(PSNRCur,'%2.2f'),'dB','    ',num2str(SSIMCur,'%2.4f')])
+        %imwrite(im2uint8(output), fullfile(folderResultCur, [nameCur, '_' num2str(imageNoiseSigma,'%02d'),'_' num2str(inputNoiseSigma,'%02d'),'_PSNR_',num2str(PSNRCur*100,'%4.0f'), extCur] ));
+        drawnow;
+        pause(pauseTime)
+    end
+    disp([filePaths(i).name,'    ',num2str(PSNRCur,'%2.2f'),'dB','    ',num2str(SSIMCur,'%2.4f')])
+    PSNRs(i) = PSNRCur;
+    SSIMs(i) = SSIMCur;
+end
+
+disp([mean(PSNRs),mean(SSIMs)]);
+
+
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/Demo_AWGN_spatially_variant_noise.m b/datasets/Test_Datasets/FFDNet-master/Demo_AWGN_spatially_variant_noise.m
new file mode 100644
index 0000000000000000000000000000000000000000..86c762c0b9421ea9246c946d662db9affacdbbfe
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/Demo_AWGN_spatially_variant_noise.m
@@ -0,0 +1,119 @@
+% This is the testing demo of FFDNet for denoising noisy grayscale images corrupted by
+% spatially variant AWGN.
+%
+% To run the code, you should install Matconvnet first. Alternatively, you can use the
+% function `vl_ffdnet_matlab` to perform denoising without Matconvnet.
+%
+% "FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising"
+%  2018/08/04
+% If you have any question, please feel free to contact with me.
+% Kai Zhang (e-mail: cskaizhang@gmail.com)
+
+%clear; clc;
+format compact;
+global sigmas; % input noise level or input noise level map
+addpath(fullfile('utilities'));
+
+folderModel = 'models';
+folderTest  = 'testsets';
+folderResult= 'results';
+imageSets   = {'BSD68','Set12'}; % testing datasets
+setTestCur  = imageSets{2};      % current testing dataset
+
+showResult  = 1;
+useGPU      = 1; % CPU or GPU. For single-threaded (ST) CPU computation, use "matlab -singleCompThread" to start matlab.
+pauseTime   = 0;
+
+load(fullfile('models','FFDNet_gray.mat'));
+net = vl_simplenn_tidy(net);
+
+% for i = 1:size(net.layers,2)
+%     net.layers{i}.precious = 1;
+% end
+
+if useGPU
+    net = vl_simplenn_move(net, 'gpu') ;
+end
+
+% read images
+ext         =  {'*.jpg','*.png','*.bmp'};
+filePaths   =  [];
+for i = 1 : length(ext)
+    filePaths = cat(1,filePaths, dir(fullfile(folderTest,setTestCur,ext{i})));
+end
+
+% PSNR and SSIM
+PSNRs = zeros(1,length(filePaths));
+SSIMs = zeros(1,length(filePaths));
+
+for i = 5
+    
+    % read images
+    label = imread(fullfile(folderTest,setTestCur,filePaths(i).name));
+    [w,h,~]=size(label);
+    if size(label,3)==3
+        label = rgb2gray(label);
+    end
+    
+    [~,nameCur,extCur] = fileparts(filePaths(i).name);
+    label = im2double(label);
+    
+    % noise level map
+    [~,~,noiseSigma] = peaks(size(label,1));
+    noiseSigma = 0  + (50 - 0).*(noiseSigma - min(noiseSigma(:)))./(max(noiseSigma(:)) - min(noiseSigma(:)));
+    noiseSigma = data_augmentation(noiseSigma, 2);
+    sigmas = imresize(noiseSigma,1/2,'bicubic')/255;
+    
+    % add noise
+    randn('seed',1);
+    noise = noiseSigma/255.*randn(size(label));
+    input = single(label + noise);
+    
+    if mod(w,2)==1
+        input = cat(1,input, input(end,:)) ;
+    end
+    if mod(h,2)==1
+        input = cat(2,input, input(:,end)) ;
+    end
+    
+    if useGPU
+        input = gpuArray(input);
+    end
+    
+    % perform denoising
+    res    = vl_simplenn(net,input,[],[],'conserveMemory',true,'mode','test'); % matconvnet default
+    % res    = vl_ffdnet_concise(net, input);    % concise version of vl_simplenn for testing FFDNet
+    % res    = vl_ffdnet_matlab(net, input); % use this if you did  not install matconvnet; very slow
+    output = res(end).x;
+    
+    
+    if mod(w,2)==1
+        output = output(1:end-1,:);
+        input  = input(1:end-1,:);
+    end
+    if mod(h,2)==1
+        output = output(:,1:end-1);
+        input  = input(:,1:end-1);
+    end
+    
+    if useGPU
+        output = gather(output);
+        input  = gather(input);
+    end
+
+    % calculate PSNR, SSIM and save results
+    [PSNRCur, SSIMCur] = Cal_PSNRSSIM(im2uint8(label),im2uint8(output),0,0);
+    if showResult
+        imshow(cat(2,im2uint8(input),im2uint8(label),im2uint8(output)));
+        title([filePaths(i).name,'    ',num2str(PSNRCur,'%2.2f'),'dB','    ',num2str(SSIMCur,'%2.4f')])
+        %imwrite(im2uint8(output), '05_sv.png');
+        drawnow;
+        pause(pauseTime)
+    end
+    disp([filePaths(i).name,'    ',num2str(PSNRCur,'%2.2f'),'dB','    ',num2str(SSIMCur,'%2.4f')])
+    PSNRs(i) = PSNRCur;
+    SSIMs(i) = SSIMCur;
+end
+
+disp([mean(PSNRs(i)),mean(SSIMs(i))]);
+
diff --git a/datasets/Test_Datasets/FFDNet-master/Demo_REAL_Color.m b/datasets/Test_Datasets/FFDNet-master/Demo_REAL_Color.m
new file mode 100644
index 0000000000000000000000000000000000000000..3eb0fb353817e49b945c61727b0130545d47c0d6
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/Demo_REAL_Color.m
@@ -0,0 +1,133 @@
+% This is the testing demo of FFDNet for denoising real noisy (color) images.
+%
+% To run the code, you should install Matconvnet first. Alternatively, you can use the
+% function `vl_ffdnet_matlab` to perform denoising without Matconvnet.
+%
+% "FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising"
+% 2018/03/23
+% If you have any question, please feel free to contact with me.
+% Kai Zhang (e-mail: cskaizhang@gmail.com)
+
+% clear; clc;
+format compact;
+global sigmas; % input noise level or input noise level map
+addpath(fullfile('utilities'));
+
+folderModel = 'models';
+folderTest  = 'testsets';
+folderResult= 'results';
+imageSets   = {'RNI15'}; % testing datasets
+setTestCur  = imageSets{1}; % current testing dataset
+
+showResult  = 1;
+useGPU      = 0;
+pauseTime   = 0;
+
+inputNoiseSigma = 15;  % input noise level (Sigma_RGB)
+
+% -****************************************************-
+% Audrey_Hepburn.jpg (inputNoiseSigma = 10); i = 1
+% Bears.png          (inputNoiseSigma = 15); i = 2
+% Boy.png            (inputNoiseSigma = 45); i = 3
+% Dog.png            (inputNoiseSigma = 28); i = 4
+% Flowers.png        (inputNoiseSigma = 70); i = 5
+% Frog.png           (inputNoiseSigma = 15); i = 6
+% Movie.png          (inputNoiseSigma = 12); i = 8
+% Pattern1.png       (inputNoiseSigma = 12); i = 9
+% Pattern2.png       (inputNoiseSigma = 40); i = 10
+% Pattern3.png       (inputNoiseSigma = 25); i = 11
+% Postcards.png      (inputNoiseSigma = 15); i = 12
+% Singer.png         (inputNoiseSigma = 30); i = 13
+% Stars.png          (inputNoiseSigma = 18); i = 14
+% Window.png         (inputNoiseSigma = 15); i = 15
+% -****************************************************-
+
+folderResultCur       =  fullfile(folderResult, [setTestCur,'_',num2str(inputNoiseSigma(1))]);
+if ~isdir(folderResultCur)
+    mkdir(folderResultCur)
+end
+
+load(fullfile('models','FFDNet_color.mat'));
+net = vl_simplenn_tidy(net);
+
+% for i = 1:size(net.layers,2)
+%     net.layers{i}.precious = 1;
+% end
+
+if useGPU
+    net = vl_simplenn_move(net, 'gpu') ;
+end
+
+% read images
+ext         =  {'*.jpg','*.png','*.bmp','*.tif'};
+filePaths   =  [];
+for i = 1 : length(ext)
+    filePaths = cat(1,filePaths, dir(fullfile(folderTest,setTestCur,ext{i})));
+end
+
+
+for i = 6
+    
+    %% read images
+    disp([filePaths(i).name])
+    label = imread(fullfile(folderTest,setTestCur,filePaths(i).name));
+    [w,h,c]=size(label);
+    
+    if c == 3
+        [~,nameCur,extCur] = fileparts(filePaths(i).name);
+        
+        input = im2single(label);
+        
+        if mod(w,2)==1
+            input = cat(1,input, input(end,:,:)) ;
+        end
+        if mod(h,2)==1
+            input = cat(2,input, input(:,end,:)) ;
+        end
+        
+        if useGPU
+            input = gpuArray(input);
+        end
+        
+        % set model noise level map
+        sigmas = inputNoiseSigma/255; % see "vl_simplenn.m".
+        
+        % perform denoising
+        res    = vl_simplenn(net,input,[],[],'conserveMemory',true,'mode','test'); % matconvnet default
+        % res    = vl_ffdnet_concise(net, input);    % concise version of vl_simplenn for testing FFDNet
+        % res    = vl_ffdnet_matlab(net, input); % use this if you did  not install matconvnet; very slow
+        
+        % output = input -res(end).x; % for 'model_color.mat'
+        output = res(end).x;
+        
+        if mod(w,2)==1
+            output = output(1:end-1,:,:);
+            input  = input(1:end-1,:,:);
+        end
+        if mod(h,2)==1
+            output = output(:,1:end-1,:);
+            input  = input(:,1:end-1,:);
+        end
+        
+        if useGPU
+            output = gather(output);
+            input  = gather(input);
+        end
+        
+        if showResult
+            imshow(cat(2,im2uint8(input),im2uint8(output)));
+            title([filePaths(i).name])
+            imwrite(im2uint8(output), fullfile(folderResultCur, [nameCur,'_' num2str(inputNoiseSigma(1),'%02d'), '.png'] ));
+            drawnow;
+            pause(pauseTime)
+        end
+        
+        
+    end
+end
+
+
+
+
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/Demo_REAL_Gray.m b/datasets/Test_Datasets/FFDNet-master/Demo_REAL_Gray.m
new file mode 100644
index 0000000000000000000000000000000000000000..fe8554c3a6c4a175f8f11c85f42fc9e8af3b911c
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/Demo_REAL_Gray.m
@@ -0,0 +1,125 @@
+% This is the testing demo of FFDNet for denoising real noisy grayscale images.
+%
+% To run the code, you should install Matconvnet first. Alternatively, you can use the
+% function `vl_ffdnet_matlab` to perform denoising without Matconvnet.
+%
+% "FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising"
+%  2018/03/23
+% If you have any question, please feel free to contact with me.
+% Kai Zhang (e-mail: cskaizhang@gmail.com)
+
+% clear; clc;
+format compact;
+global sigmas; % input noise level or input noise level map
+addpath(fullfile('utilities'));
+
+folderModel = 'models';
+folderTest  = 'testsets';
+folderResult= 'results';
+imageSets   = {'RNI6'};         % testing datasets
+setTestCur  = imageSets{1};      % current testing dataset
+
+showResult  = 1;
+useGPU      = 1;
+pauseTime   = 0;
+
+
+inputNoiseSigma = 15;  % input noise level
+% -****************************************************-
+% Building.png        (inputNoiseSigma = 20); i = 1
+% Chupa_Chups.png     (inputNoiseSigma = 10); i = 2
+% David_Hilbert.png   (inputNoiseSigma = 15); i = 3
+% Marilyn.png         (inputNoiseSigma = 7);  i = 4
+% Old_Tom_Morris.png  (inputNoiseSigma = 15); i = 5
+% Vinegar.png         (inputNoiseSigma = 20); i = 6
+% -****************************************************-
+
+folderResultCur       =  fullfile(folderResult, [setTestCur,'_',num2str(inputNoiseSigma)]);
+if ~isdir(folderResultCur)
+    mkdir(folderResultCur)
+end
+
+load(fullfile('models','FFDNet_gray.mat'));
+net = vl_simplenn_tidy(net);
+
+% for i = 1:size(net.layers,2)
+%     net.layers{i}.precious = 1;
+% end
+
+if useGPU
+    net = vl_simplenn_move(net, 'gpu') ;
+end
+
+% read images
+ext         =  {'*.jpg','*.png','*.bmp'};
+filePaths   =  [];
+for i = 1 : length(ext)
+    filePaths = cat(1,filePaths, dir(fullfile(folderTest,setTestCur,ext{i})));
+end
+
+
+for i = 1
+    
+    % read images
+    disp([filePaths(i).name])
+    label = imread(fullfile(folderTest,setTestCur,filePaths(i).name));
+    [w,h,~]=size(label);
+    if size(label,3)==3
+        label = rgb2gray(label);
+    end
+    
+    [~,nameCur,extCur] = fileparts(filePaths(i).name);
+    input = im2single(label);
+    
+    if mod(w,2)==1
+        input = cat(1,input, input(end,:)) ;
+    end
+    if mod(h,2)==1
+        input = cat(2,input, input(:,end)) ;
+    end
+    
+    % tic;
+    if useGPU
+        input = gpuArray(input);
+    end
+    
+    % set noise level map
+    sigmas = inputNoiseSigma/255; % see "vl_simplenn.m".
+    
+    % perform denoising
+    res    = vl_simplenn(net,input,[],[],'conserveMemory',true,'mode','test'); % matconvnet default
+    % res    = vl_ffdnet_concise(net, input);    % concise version of vl_simplenn for testing FFDNet
+    % res    = vl_ffdnet_matlab(net, input); % use this if you did  not install matconvnet; very slow
+    
+    % output = input - res(end).x; % for 'model_gray.mat'
+    output = res(end).x;
+    
+    if mod(w,2)==1
+        output = output(1:end-1,:);
+        input  = input(1:end-1,:);
+    end
+    if mod(h,2)==1
+        output = output(:,1:end-1);
+        input  = input(:,1:end-1);
+    end
+    
+    % convert to CPU
+    if useGPU
+        output = gather(output);
+        input  = gather(input);
+    end
+    % toc;
+    if showResult
+        imshow(cat(2,im2uint8(input),im2uint8(output)));
+        title([filePaths(i).name])
+        imwrite(im2uint8(output), fullfile(folderResultCur, [nameCur, '_' num2str(inputNoiseSigma,'%02d'), '.png'] ));
+        drawnow;
+        pause(pauseTime)
+    end
+    
+    
+end
+
+
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/Demo_multivariate_Gaussian_noise.m b/datasets/Test_Datasets/FFDNet-master/Demo_multivariate_Gaussian_noise.m
new file mode 100644
index 0000000000000000000000000000000000000000..c2bc5a2d2731a0ba93720308c7022b267850daa0
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/Demo_multivariate_Gaussian_noise.m
@@ -0,0 +1,193 @@
+% This is the testing demo of FFDNet for denoising noisy color images corrupted by
+% multivariate (3D) Gaussian noise model N([0,0,0]; Sigma) with zero mean and 
+% covariance matrix Sigma in the RGB color space.
+%
+% To run the code, you should install Matconvnet first. Alternatively, you can use the
+% function `vl_ffdnet_matlab` to perform denoising without Matconvnet.
+%
+% "FFDNet: Toward a Fast and Flexible Solution for CNN based Image
+% Denoising" 2018/03/23
+% If you have any question, please feel free to contact with me.
+% Kai Zhang (e-mail: cskaizhang@gmail.com)
+
+% clear; clc;
+
+format compact;
+global sigmas; % input noise level or input noise level map
+addpath(fullfile('utilities'));
+
+folderModel = 'models';
+folderTest  = 'testsets';
+folderResult= 'results';
+imageSets   = {'BSD68','Set12','CBSD68','Kodak24','McMaster'}; % testing datasets
+setTestCur  = imageSets{3};      % current testing dataset
+
+showResult  = 1;
+useGPU      = 1;
+pauseTime   = 0;
+
+%% Sigma
+%% noise case 1 (general)
+%   [(sigma_R)^2 &    sigma_RG     & sigma_RB 
+%    sigma_RG   &   (sigma_G)^2   & sigma_GB
+%    sigma_RB   &    sigma_GB     & (sigma_B)^2]
+
+%% noise case 2 (special) channel-independent noise,  the widely-used noise model for color image
+%   [(sigma)^2   &    0            &  0
+%    0           &   (sigma)^2     &  0
+%    0           &    0            & (sigma)^2]
+
+%% noise case 3 (special) channel-independent noise
+%   [(sigma_R)^2 &    0            &  0
+%    0           &   (sigma_G)^2   &  0
+%    0           &    0            & (sigma_B)^2]
+
+%% noise case 4 (special) each channel has the same noise,  the widely-used noise model for grayscale image
+%   [(sigma)^2   &   (sigma)^2     &  (sigma)^2
+%    (sigma)^2   &   (sigma)^2     &  (sigma)^2
+%    (sigma)^2   &   (sigma)^2     & (sigma)^2]
+
+
+% image noise Sigma
+noisecase = 2;
+
+switch noisecase
+    case 1
+        L = 75/255;
+        D = diag(rand(3,1));
+        U = orth(rand(3,3));
+        imageNoiseSigma = abs(L^2*(U' * D * U));
+    case 2
+        noiseLevelRGB = 50;
+        imageNoiseSigma = (noiseLevelRGB/255)^2*eye(3);
+    case 3
+        noiseLevelR = 15;
+        noiseLevelG = 30;
+        noiseLevelB = 45;
+        imageNoiseSigma = [(noiseLevelR/255)^2, 0, 0; 0, (noiseLevelG/255)^2, 0; 0, 0, (noiseLevelB/255)^2];
+    case 4 
+        noiseLevel = 50;
+        imageNoiseSigma = (noiseLevel/255)^2*ones(3);
+end
+
+assert(sum(sum(imageNoiseSigma<0)) == 0,'We assume that all the elements are non-negative.');
+
+% input noise Sigma
+inputNoiseSigma =  imageNoiseSigma;
+sigma           =  sqrt(inputNoiseSigma);
+sigma           =  sigma(:);
+sigma6          =  sigma([1,4,5,7,8,9]); % extract 6 parameters
+
+
+folderResultCur       =  fullfile(folderResult, [setTestCur,'_',num2str(imageNoiseSigma(1,1)),'_',num2str(inputNoiseSigma(1,1))]);
+if ~isdir(folderResultCur)
+    mkdir(folderResultCur)
+end
+
+load(fullfile('models','FFDNet_3D_RGB.mat'));
+net = vl_simplenn_tidy(net);
+
+% for i = 1:size(net.layers,2)
+%     net.layers{i}.precious = 1;
+% end
+
+if useGPU
+    net = vl_simplenn_move(net, 'gpu') ;
+end
+
+% read images
+ext         =  {'*.jpg','*.png','*.bmp','*.tif'};
+filePaths   =  [];
+for i = 1 : length(ext)
+    filePaths = cat(1,filePaths, dir(fullfile(folderTest,setTestCur,ext{i})));
+end
+
+% PSNR and SSIM
+PSNRs = zeros(1,length(filePaths));
+SSIMs = zeros(1,length(filePaths));
+
+for i = 1:length(filePaths)
+    
+    % read images
+    label   = imread(fullfile(folderTest,setTestCur,filePaths(i).name));
+    [w,h,c] = size(label);
+    label   = im2double(label);
+    if c == 1
+        label = cat(3,label,label,label);
+    end
+    
+    [~,nameCur,extCur] = fileparts(filePaths(i).name);
+    
+    % add noise
+    randn('seed',0);
+    % noise = bsxfun(@times,randn(size(label)),permute(imageNoiseSigma/255,[3 4 1 2]));
+    
+    noise = vl_randnm(w, h, imageNoiseSigma);
+    
+    input = single(label + noise);
+    
+    if mod(w,2)==1
+        input = cat(1,input, input(end,:,:)) ;
+    end
+    if mod(h,2)==1
+        input = cat(2,input, input(:,end,:)) ;
+    end
+    
+    % tic;
+    if useGPU
+        input = gpuArray(input);
+    end
+    
+    % set noise level map
+    sigmas = sigma6; % see "vl_simplenn.m".
+    
+    % perform denoising
+    res    = vl_simplenn(net,input,[],[],'conserveMemory',true,'mode','test'); % matconvnet default
+    % res    = vl_ffdnet_concise(net, input);    % concise version of vl_simplenn for testing FFDNet
+    %res    = vl_ffdnet_matlab(net, input); % use this if you did  not install matconvnet; very slow
+    
+    % output = input -res(end).x; % for 'model_color.mat'
+    output = res(end).x;
+    
+    
+    if mod(w,2)==1
+        output = output(1:end-1,:,:);
+        input  = input(1:end-1,:,:);
+    end
+    if mod(h,2)==1
+        output = output(:,1:end-1,:);
+        input  = input(:,1:end-1,:);
+    end
+    
+    if useGPU
+        output = gather(output);
+        input  = gather(input);
+    end
+    %toc;
+    
+    if c == 1
+        output= mean(output,3);
+        input = mean(input,3);
+        label = mean(label,3);
+    end
+    
+    % calculate PSNR, SSIM and save results
+    [PSNRCur, SSIMCur] = Cal_PSNRSSIM(im2uint8(label),im2uint8(output),0,0);
+    if showResult
+        imshow(cat(2,im2uint8(input),im2uint8(label),im2uint8(output)));
+        title([filePaths(i).name,'    ',num2str(PSNRCur,'%2.2f'),'dB','    ',num2str(SSIMCur,'%2.4f')])
+        %imwrite(im2uint8(output), fullfile(folderResultCur, [nameCur, '_' num2str(imageNoiseSigma(1,1),'%02d'),'_' num2str(inputNoiseSigma(1,1),'%02d'),'_PSNR_',num2str(PSNRCur*100,'%4.0f'), extCur] ));
+        drawnow;
+        pause(pauseTime)
+    end
+    disp([filePaths(i).name,'    ',num2str(PSNRCur,'%2.2f'),'dB','    ',num2str(SSIMCur,'%2.4f')])
+    PSNRs(i) = PSNRCur;
+    SSIMs(i) = SSIMCur;
+    
+end
+
+disp([mean(PSNRs),mean(SSIMs)]);
+
+
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/README.md b/datasets/Test_Datasets/FFDNet-master/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..f0b1ba2b3030da2a8e5477284bcbfe949ba91356
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/README.md
@@ -0,0 +1,156 @@
+# FFDNet
+
+![visitors](https://visitor-badge.glitch.me/badge?page_id=cszn/FFDNet) 
+
+# [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://arxiv.org/pdf/1710.04026.pdf)
+
+# New training and testing codes ([PyTorch](https://github.com/cszn/KAIR)) - 18/12/2019
+- [main_train_ffdnet.py](https://github.com/cszn/KAIR/blob/master/main_train_ffdnet.py)
+
+- [main_test_ffdnet.py](https://github.com/cszn/KAIR/blob/master/main_test_ffdnet.py)
+
+# Training and Testing Codes ([PyTorch](http://www.ipol.im/pub/pre/231/))
+
+## [FFDNet-pytorch](http://www.ipol.im/pub/pre/231/)
+### [An Analysis and Implementation of the FFDNet Image Denoising Method](http://www.ipol.im/pub/pre/231/)
+
+# PixelUnshuffle layer (PyTorch)
+
+``` python
+from torch.nn import Module
+
+
+def pixel_unshuffle(input, upscale_factor):
+    r"""Rearranges elements in a Tensor of shape :math:`(C, rH, rW)` to a
+    tensor of shape :math:`(*, r^2C, H, W)`.
+    written by: Zhaoyi Yan, https://github.com/Zhaoyi-Yan
+    and Kai Zhang, https://github.com/cszn/FFDNet
+    01/01/2019
+    """
+    batch_size, channels, in_height, in_width = input.size()
+
+    out_height = in_height // upscale_factor
+    out_width = in_width // upscale_factor
+
+    input_view = input.contiguous().view(
+        batch_size, channels, out_height, upscale_factor,
+        out_width, upscale_factor)
+
+    channels *= upscale_factor ** 2
+    unshuffle_out = input_view.permute(0, 1, 3, 5, 2, 4).contiguous()
+    return unshuffle_out.view(batch_size, channels, out_height, out_width)
+
+
+class PixelUnShuffle(Module):
+    r"""Rearranges elements in a Tensor of shape :math:`(C, rH, rW)` to a
+    tensor of shape :math:`(*, r^2C, H, W)`.
+    written by: Zhaoyi Yan, https://github.com/Zhaoyi-Yan
+    and Kai Zhang, https://github.com/cszn/FFDNet
+    01/01/2019
+    """
+
+    def __init__(self, upscale_factor):
+        super(PixelUnShuffle, self).__init__()
+        self.upscale_factor = upscale_factor
+
+    def forward(self, input):
+        return pixel_unshuffle(input, self.upscale_factor)
+
+    def extra_repr(self):
+        return 'upscale_factor={}'.format(self.upscale_factor)
+
+```
+
+
+# Abstract
+Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for denoising images with different noise levels. They also lack flexibility to deal with spatially variant noise, limiting their applications in practical denoising. 
+To address these issues, we present a fast and flexible denoising convolutional neural network, namely FFDNet, with a tunable noise level map as the input. 
+The proposed FFDNet works on downsampled subimages,achieving a good trade-off between inference speed and
+denoising performance. In contrast to the existing discriminative denoisers, FFDNet enjoys several desirable properties, including
+
+- the ability to handle a wide range of noise levels (i.e., [0, 75]) effectively with a single network, 
+- the ability to remove spatially variant noise by specifying a non-uniform noise level map, and 
+- faster speed than benchmark BM3D even on CPU without sacrificing denoising performance. 
+
+Extensive experiments on synthetic and real noisy images are conducted to evaluate FFDNet in comparison with state-of-the-art denoisers. The results show that FFDNet is effective and efficient, making it highly attractive for practical denoising applications.
+
+# Network Architecture
+![architecture](https://github.com/cszn/FFDNet/blob/master/figs/ffdnet.png)
+The input image is reshaped to four sub-images, which are then input to the CNN together with a noise level map. The final output is reconstructed by the four denoised sub-images
+
+# Test FFDNet Models
+- [Demo_AWGN_Gray.m](Demo_AWGN_Gray.m) is the testing demo of FFDNet for denoising grayscale images corrupted by AWGN.
+- [Demo_AWGN_Color.m](Demo_AWGN_Color.m) is the testing demo of FFDNet for denoising color images corrupted by AWGN.
+
+- [Demo_AWGN_Gray_Clip.m](Demo_AWGN_Gray_Clip.m) is the testing demo of FFDNet for denoising grayscale images corrupted by AWGN with clipping setting.
+- [Demo_AWGN_Color_Clip.m](Demo_AWGN_Color_Clip.m) is the testing demo of FFDNet for denoising color images corrupted by AWGN with clipping setting.
+
+- [Demo_REAL_Gray.m](Demo_REAL_Gray.m) is the testing demo of FFDNet for denoising real noisy (grayscale) images.
+- [Demo_REAL_Color.m](Demo_REAL_Color.m) is the testing demo of FFDNet for denoising real noisy (color) images.
+
+- [Demo_multivariate_Gaussian_noise.m](Demo_multivariate_Gaussian_noise.m) is the testing demo of FFDNet for denoising noisy images corrupted by multivariate (3D) Gaussian noise model `N([0,0,0]; Sigma)` with zero mean and covariance matrix `Sigma` in the RGB color space.
+
+
+# Results on Real Noisy Images from [The Darmstadt Noise Dataset](https://noise.visinf.tu-darmstadt.de/benchmark/#results_srgb)
+
+## PSNR: 37.61dB
+
+
+The left is the noisy image from [The Darmstadt Noise Dataset](https://noise.visinf.tu-darmstadt.de/benchmark/#results_srgb). The right is the denoised image by FFDNet+.
+
+<img src="figs/0001_18.png" width="400px"/> <img src="figs/0001_18_ffdnet.png" width="400px"/>
+
+<img src="figs/0033_05.png" width="400px"/> <img src="figs/0033_05_ffdnet.png" width="400px"/>
+
+<img src="figs/0009_15.png" width="400px"/> <img src="figs/0009_15_ffdnet.png" width="400px"/>
+
+<img src="figs/0037_12.png" width="400px"/> <img src="figs/0037_12_ffdnet.png" width="400px"/>
+
+
+# Image Denoising for AWGN
+
+Grayscale Image Denoising
+
+<img src="figs/table1.png" width="500px"/> 
+
+Color Image Denoising
+
+<img src="figs/table2.png" width="500px"/> 
+
+The left is the noisy image corrupted by AWGN with noise level 75. The right is the denoised image by FFDNet.
+
+<img src="utilities/figs/102061_75_75.png" width="321px"/> <img src="utilities/figs/102061_75_75_PSNR_2698.png" width="321px"/>
+
+# Real Image Denoising
+
+The left is the real noisy image. The right is the denoised image by FFDNet.
+
+<img src="utilities/figs/David_Hilbert.jpg" width="321px"/> <img src="utilities/figs/David_Hilbert_15.png" width="321px"/>
+
+![example](https://github.com/cszn/FFDNet/blob/master/utilities/figs/Frog.gif)
+
+# Extension
+
+<img src="models/multivariate Gaussian noise .png" width="500px"/> 
+
+- [Demo_multivariate_Gaussian_noise.m](Demo_multivariate_Gaussian_noise.m) is the testing demo of FFDNet for denoising noisy images corrupted by multivariate (3D) Gaussian noise model `N([0,0,0]; Sigma)` with zero mean and covariance matrix `Sigma` in the RGB color space.
+
+# Requirements and Dependencies
+To run the code, you should install Matconvnet first. 
+Alternatively, you can use function [vl_ffdnet_matlab](utilities/vl_ffdnet_matlab.m) to perform denoising without Matconvnet.
+- MATLAB R2015b
+- [Cuda](https://developer.nvidia.com/cuda-toolkit-archive)-8.0 & [cuDNN](https://developer.nvidia.com/cudnn) v-5.1
+- [MatConvNet](http://www.vlfeat.org/matconvnet/)
+
+# Citation
+```
+@article{zhang2018ffdnet,
+  title={FFDNet: Toward a Fast and Flexible Solution for {CNN} based Image Denoising},
+  author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+  journal={IEEE Transactions on Image Processing},
+  year={2018},
+}
+```
+
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/Demo_Test_FFDNet_gray.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/Demo_Test_FFDNet_gray.m
new file mode 100644
index 0000000000000000000000000000000000000000..6666c3b7bbc5d0b4aadaf4871e82e525a19e31c9
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/Demo_Test_FFDNet_gray.m
@@ -0,0 +1,132 @@
+% This is the testing demo of FFDNet for denoising noisy grayscale images corrupted by
+% AWGN.
+%
+% To run the code, you should install Matconvnet first. Alternatively, you can use the
+% function `vl_ffdnet_matlab` to perform denoising without Matconvnet.
+%
+% "FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising"
+%  2018/03/23
+% If you have any question, please feel free to contact with me.
+% Kai Zhang (e-mail: cskaizhang@gmail.com)
+
+%clear; clc;
+format compact;
+global sigmas; % input noise level or input noise level map
+addpath(fullfile('utilities'));
+
+folderModel = 'models';
+folderTest  = 'H:\matlabH';
+folderResult= 'results';
+imageSets   = {'BSD68','Set12'}; % testing datasets
+setTestCur  = imageSets{1};      % current testing dataset
+
+showResult  = 1;
+useGPU      = 1; % CPU or GPU. For single-threaded (ST) CPU computation, use "matlab -singleCompThread" to start matlab.
+pauseTime   = 0;
+
+imageNoiseSigma = 25;  % image noise level
+inputNoiseSigma = 25;  % input noise level
+
+folderResultCur       =  fullfile(folderResult, [setTestCur,'_',num2str(imageNoiseSigma),'_',num2str(inputNoiseSigma)]);
+if ~isdir(folderResultCur)
+    mkdir(folderResultCur)
+end
+
+load(fullfile('data','FFDNet_gray','FFDNet_gray-epoch-1.mat'));
+net.layers = net.layers(1:end-1);
+net = vl_simplenn_tidy(net);
+
+% for i = 1:size(net.layers,2)
+%     net.layers{i}.precious = 1;
+% end
+
+if useGPU
+    net = vl_simplenn_move(net, 'gpu') ;
+end
+
+% read images
+ext         =  {'*.jpg','*.png','*.bmp'};
+filePaths   =  [];
+for i = 1 : length(ext)
+    filePaths = cat(1,filePaths, dir(fullfile(folderTest,setTestCur,ext{i})));
+end
+
+% PSNR and SSIM
+PSNRs = zeros(1,length(filePaths));
+SSIMs = zeros(1,length(filePaths));
+
+for i = 1:length(filePaths)
+    
+    % read images
+    label = imread(fullfile(folderTest,setTestCur,filePaths(i).name));
+    [w,h,~]=size(label);
+    if size(label,3)==3
+        label = rgb2gray(label);
+    end
+    
+    [~,nameCur,extCur] = fileparts(filePaths(i).name);
+    label = im2double(label);
+    
+    % add noise
+    randn('seed',0);
+    noise = imageNoiseSigma/255.*randn(size(label));
+    input = single(label + noise);
+    
+    if mod(w,2)==1
+        input = cat(1,input, input(end,:)) ;
+    end
+    if mod(h,2)==1
+        input = cat(2,input, input(:,end)) ;
+    end
+    
+    % tic;
+    if useGPU
+        input = gpuArray(input);
+    end
+    
+    % set noise level map
+    sigmas = inputNoiseSigma/255; % see "vl_simplenn.m".
+    
+    % perform denoising
+    res    = vl_simplenn(net,input,[],[],'conserveMemory',true,'mode','test'); % matconvnet default
+    % res    = vl_ffdnet_concise(net, input);    % concise version of vl_simplenn for testing FFDNet
+    % res    = vl_ffdnet_matlab(net, input); % use this if you did  not install matconvnet; very slow
+    
+    
+    % output = input - res(end).x; % for 'model_gray.mat'
+    output = res(end).x;
+    
+    
+    if mod(w,2)==1
+        output = output(1:end-1,:);
+        input  = input(1:end-1,:);
+    end
+    if mod(h,2)==1
+        output = output(:,1:end-1);
+        input  = input(:,1:end-1);
+    end
+    
+    if useGPU
+        output = gather(output);
+        input  = gather(input);
+    end
+    % toc;
+    % calculate PSNR, SSIM and save results
+    [PSNRCur, SSIMCur] = Cal_PSNRSSIM(im2uint8(label),im2uint8(output),0,0);
+    if showResult
+        imshow(cat(2,im2uint8(input),im2uint8(label),im2uint8(output)));
+        title([filePaths(i).name,'    ',num2str(PSNRCur,'%2.2f'),'dB','    ',num2str(SSIMCur,'%2.4f')])
+        %imwrite(im2uint8(output), fullfile(folderResultCur, [nameCur, '_' num2str(imageNoiseSigma,'%02d'),'_' num2str(inputNoiseSigma,'%02d'),'_PSNR_',num2str(PSNRCur*100,'%4.0f'), extCur] ));
+        drawnow;
+        pause(pauseTime)
+    end
+    disp([filePaths(i).name,'    ',num2str(PSNRCur,'%2.2f'),'dB','    ',num2str(SSIMCur,'%2.4f')])
+    PSNRs(i) = PSNRCur;
+    SSIMs(i) = SSIMCur;
+end
+
+disp([mean(PSNRs),mean(SSIMs)]);
+
+
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/Demo_Train_FFDNet_gray.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/Demo_Train_FFDNet_gray.m
new file mode 100644
index 0000000000000000000000000000000000000000..f80fb0df25384bbeaea48af0e41fc4b84b6f82c4
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/Demo_Train_FFDNet_gray.m
@@ -0,0 +1,67 @@
+% This is the training demo of FFDNet for denoising noisy grayscale images corrupted by
+% AWGN.
+%
+% To run the code, you should install Matconvnet (http://www.vlfeat.org/matconvnet/) first.
+%
+% @article{zhang2018ffdnet,
+%   title={FFDNet: Toward a Fast and Flexible Solution for {CNN} based Image Denoising},
+%   author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+%   journal={IEEE Transactions on Image Processing},
+%   volume={27}, 
+%   number={9}, 
+%   pages={4608-4622}, 
+% }
+%
+% If you have any question, please feel free to contact with me.
+% Kai Zhang (e-mail: cskaizhang@gmail.com)
+
+%% ********************  Note  **********************************
+% ** You should set the training images folders of "generatepatches.m" first. Then you can run "Demo_Train_FFDNet_gray.m" directly.
+% **
+% ** folders    = {'path_of_your_training_dataset'};% set this from "generatepatches.m" first!
+% ** stride     = 10;                       % control the number of image patches, from "generatepatches.m"
+% ** nimages  = round(length(filepaths));   % control the number of image patches, from "generatepatches.m"
+% **
+%% **************************************************************
+
+
+
+
+format compact;
+addpath('utilities');
+
+%-------------------------------------------------------------------------
+% Configuration
+%-------------------------------------------------------------------------
+
+opts.modelName        = 'FFDNet_gray'; % model name
+opts.learningRate     = [logspace(-4,-4,100),logspace(-4,-4,100)/3,logspace(-4,-4,100)/(3^2),logspace(-4,-4,100)/(3^3),logspace(-4,-4,100)/(3^4)];% you can change the learning rate
+opts.batchSize        = 64; % default  
+opts.gpus             = [1]; % this code can only support one GPU!
+opts.numSubBatches    = 2;
+opts.weightDecay      = 0.0005;
+opts.expDir           = fullfile('data', opts.modelName);
+
+%-------------------------------------------------------------------------
+%  Initialize model
+%-------------------------------------------------------------------------
+
+net  = feval(['model_init_',opts.modelName]);
+
+%-------------------------------------------------------------------------
+%   Train
+%-------------------------------------------------------------------------
+
+[net, info] = model_train(net,  ...
+    'expDir', opts.expDir, ...
+    'learningRate',opts.learningRate, ...
+    'numSubBatches',opts.numSubBatches, ...
+    'weightDecay',opts.weightDecay, ...
+    'batchSize', opts.batchSize, ...
+    'modelname', opts.modelName, ...
+    'gpus',opts.gpus) ;
+
+
+
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/generatepatches.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/generatepatches.m
new file mode 100644
index 0000000000000000000000000000000000000000..12ed9a77c36b6eb900984d58a787af8359f1d6ec
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/generatepatches.m
@@ -0,0 +1,96 @@
+function [imdb] = generatepatches
+
+%% Note, set your training image set first, large dataset is prefered!
+folders    = {'path_of_your_training_dataset'}; % set this first!
+
+stride     = 60;  % control the number of image patches
+patchsize  = 70;
+
+batchSize  = 256; % important for BNorm
+count      = 0;
+nch        = 1;   % 1 for grayscale image, 3 for color image
+
+step1      = 0;
+step2      = 0;
+
+ext               =  {'*.jpg','*.png','*.bmp'};
+filepaths         =  [];
+
+for j = 1:length(folders)
+    for i = 1 : length(ext)
+        filepaths = cat(1,filepaths, dir(fullfile(folders{j}, ext{i})));
+    end
+end
+
+cshuffle = randperm(length(filepaths));
+nimages  = round(length(filepaths)); % control the number of image patches
+
+ns       = 1;
+nscales  = min(1,0.45 + 0.05*randi(21,[ns,nimages]));
+naugment = randi(8,[1,nimages]);
+
+for i = 1 : nimages
+    % HR = imread(fullfile(filepaths(cshuffle(i)).folder,filepaths(cshuffle(i)).name));
+    HR = imread(fullfile(folders{1},filepaths(cshuffle(i)).name));
+    HR = HR(:,:,1);
+    
+    HR = data_augmentation(HR, naugment(i));
+    disp([i,nimages,round(count/batchSize)])
+    
+    for j = 1: size(nscales,1)
+        
+        HR_current  = imresize(HR,nscales(j,i),'bicubic');
+        [hei,wid,~] = size(HR_current);
+        for x = 1+step1 : stride : (hei-patchsize+1)
+            for y = 1+step2 : stride : (wid-patchsize+1)
+                count=count+1;
+            end
+        end
+    end
+end
+
+
+numPatches  = ceil(count/batchSize)*batchSize;
+diffPatches = numPatches - count;
+disp([numPatches,numPatches/batchSize,diffPatches]);
+
+disp('-----------------------------');
+
+%------------------------------------------------------------------
+%------------------------------------------------------------------
+
+count = 0;
+imdb.HRlabels  = zeros(patchsize, patchsize, nch, numPatches,'single');
+
+for i = 1 : nimages
+    % HR = imread(fullfile(filepaths(cshuffle(i)).folder,filepaths(cshuffle(i)).name));
+    HR = imread(fullfile(folders{1},filepaths(cshuffle(i)).name));
+    
+    if nch ==1 && size(HR,3) == 3
+        HR = rgb2gray(HR);
+    end
+    
+    HR = data_augmentation(HR, naugment(i));
+    disp([i,nimages,round(count/256)])
+    
+    for j = 1: size(nscales,1)
+        
+        HR_current  = imresize(HR,nscales(j,i),'bicubic');
+        [hei,wid,~] = size(HR_current);
+        HR_current  = im2single(HR_current);
+        
+        for x = 1+step1 : stride : (hei-patchsize+1)
+            for y = 1+step2 : stride : (wid-patchsize+1)
+                count = count + 1;
+                subim_label  = HR_current(x : x+patchsize-1, y : y+patchsize-1,1:nch);
+                imdb.HRlabels(:, :, :, count) = subim_label;
+                if count<=diffPatches
+                    imdb.HRlabels(:, :, :, end-count+1)   = HR_current(x : x+patchsize-1, y : y+patchsize-1,1:nch);
+                end
+            end
+        end
+    end
+end
+
+imdb.set    = uint8(ones(1,size(imdb.HRlabels,4)));
+
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/model_init_FFDNet_gray.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/model_init_FFDNet_gray.m
new file mode 100644
index 0000000000000000000000000000000000000000..32ba47f607440a84221c49c5de00c4890914863d
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/model_init_FFDNet_gray.m
@@ -0,0 +1,117 @@
+
+function net = model_init_FFDNet_gray
+
+
+lr11  = [1 1];
+lr10  = [1 0];
+weightDecay = [1 1];
+nCh = 64; % number of channels
+fSz = 3;  % fize size
+nNm = 1;  % number of noise level map
+
+useBnorm  = 0; % if useBnorm  = 0, you should also use adam.
+nsubimage = 4; % 4 for grayscale image, 12 for color image
+
+% Define network
+
+net.layers = {} ;
+
+net.layers{end+1} = struct('type', 'SubP','scale',1/2) ;
+
+net.layers{end+1} = struct('type', 'concat') ;
+
+net.layers{end+1} = struct('type', 'conv', ...
+    'weights', {{orthrize2(sqrt(2/(9*nCh))*randn(fSz,fSz,nsubimage+nNm,nCh,'single')),  zeros(nCh,1,'single')}}, ...
+    'stride', 1, ...
+    'pad', 1, ...
+    'dilate',1, ...
+    'learningRate',lr11, ...
+    'weightDecay',weightDecay, ...
+    'opts',{{}}) ;
+
+net.layers{end+1} = struct('type', 'relu','leak',0) ;
+
+for i = 1:1:13
+    
+    if useBnorm == 0
+        
+        net.layers{end+1} = struct('type', 'conv', ...
+            'weights', {{orthrize2(sqrt(2/(9*nCh))*randn(fSz,fSz,nCh,nCh,'single')), zeros(nCh,1,'single')}}, ...
+            'stride', 1, ...
+            'learningRate',lr11, ...
+            'dilate',1, ...
+            'weightDecay',weightDecay, ...
+            'pad', 1, 'opts', {{}}) ;
+        
+    else
+        
+        net.layers{end+1} = struct('type', 'conv', ...
+            'weights', {{orthrize2(sqrt(2/(9*nCh))*randn(fSz,fSz,nCh,nCh,'single')), zeros(nCh,1,'single')}}, ...
+            'stride', 1, ...
+            'learningRate',lr10, ...
+            'dilate',1, ...
+            'weightDecay',weightDecay, ...
+            'pad', 1, 'opts', {{}}) ;
+        
+        net.layers{end+1} = struct('type', 'bnorm', ...
+            'weights', {{ones(nCh,1,'single'), zeros(nCh,1,'single'),[zeros(nCh,1,'single'), ones(nCh,1,'single')]}}, ...
+            'learningRate', [1 1 1], ...
+            'weightDecay', [0 0]) ;
+        
+    end
+    
+    net.layers{end+1} = struct('type', 'relu','leak',0) ;
+    
+end
+
+
+net.layers{end+1} = struct('type', 'conv', ...
+    'weights', {{orthrize2(sqrt(2/(9*nCh))*randn(fSz,fSz,nCh,nsubimage,'single')), zeros(nsubimage,1,'single')}}, ...
+    'stride', 1, ...
+    'learningRate',lr10, ...
+    'dilate',1, ...
+    'weightDecay',weightDecay, ...
+    'pad', 1, 'opts', {{}}) ;
+
+net.layers{end+1} = struct('type', 'SubP','scale',2) ;
+
+
+net.layers{end+1} = struct('type', 'loss') ; % make sure the new 'vl_nnloss.m' is in the same folder.
+
+% Fill in default values
+net = vl_simplenn_tidy(net);
+
+end
+
+
+function W = orthrize2(a)
+
+s_ = size(a);
+a = reshape(a,[size(a,1)*size(a,2)*size(a,3),size(a,4),1,1]);
+[u,d,v] = svd(a, 'econ');
+if(size(a,1) < size(a, 2))
+    u = v';
+end
+%W = sqrt(2).*reshape(u, s_);
+W = reshape(u, s_);
+
+end
+
+
+function A = clipping2(A,b)
+
+A(A<b(1)) = b(1);
+A(A>b(2)) = b(2);
+
+end
+
+
+
+function A = clipping(A,b)
+
+A(A>=0&A<b) = b;
+A(A<0&A>-b) = -b;
+
+end
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/model_train.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/model_train.m
new file mode 100644
index 0000000000000000000000000000000000000000..82c1d972132164a70b6c98eb5a2bce8d714ee7b6
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/model_train.m
@@ -0,0 +1,285 @@
+function [net, state] = model_train(net, varargin)
+
+
+% simple code 
+
+%    The function automatically restarts after each training epoch by
+%    checkpointing.
+%
+%    The function supports training on CPU or on one or more GPUs
+%    (specify the list of GPU IDs in the `gpus` option).
+
+% Copyright (C) 2014-16 Andrea Vedaldi.
+% All rights reserved.
+%
+% This file is part of the VLFeat library and is made available under
+% the terms of the BSD license (see the COPYING file).
+
+%-------------------------------------------------------------------------
+% solver:  Adam
+%-------------------------------------------------------------------------
+
+opts.beta1        = 0.9;
+opts.beta2        = 0.999;
+opts.alpha        = 0.01;
+opts.epsilon      = 1e-8;
+opts.weightDecay  = 0.0001;
+
+%-------------------------------------------------------------------------
+%  setting for simplenn
+%-------------------------------------------------------------------------
+
+opts.conserveMemory = true;
+opts.mode           = 'normal';
+opts.cudnn          = true ;
+opts.backPropDepth  = +inf ;
+opts.skipForward    = false;
+opts.numSubBatches  = 1;
+
+%-------------------------------------------------------------------------
+%  setting for model
+%-------------------------------------------------------------------------
+
+opts.batchSize  = 128 ;
+opts.gpus       = [];
+opts.numEpochs  = 300 ;
+opts.learningRate = 0.0001*ones(1,100,'single');
+opts.modelName  = 'model';
+opts.expDir     = fullfile('data',opts.modelName) ;
+
+%-------------------------------------------------------------------------
+%  update settings
+%-------------------------------------------------------------------------
+
+opts            = vl_argparse(opts, varargin);
+opts.numEpochs  = numel(opts.learningRate);
+
+if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir) ; end
+
+%-------------------------------------------------------------------------
+% Initialization
+%-------------------------------------------------------------------------
+
+net = vl_simplenn_tidy(net);    %%% fill in some eventually missing values
+net.layers{end-1}.precious = 1;
+vl_simplenn_display(net, 'batchSize', opts.batchSize) ;
+
+state.getBatch = getBatch ;
+
+%-------------------------------------------------------------------------
+% Train and Test
+%-------------------------------------------------------------------------
+
+modelPath = @(ep) fullfile(opts.expDir, sprintf([opts.modelName,'-epoch-%d.mat'], ep));
+
+start = findLastCheckpoint(opts.expDir,opts.modelName) ;
+if start >= 1
+    fprintf('%s: resuming by loading epoch %d', mfilename, start) ;
+    load(modelPath(start), 'net') ;
+    % net = vl_simplenn_tidy(net) ;
+end
+
+imdb = [];
+
+for epoch = start+1 : opts.numEpochs
+    
+    % Train for one epoch.
+    state.epoch = epoch ;
+    state.learningRate = opts.learningRate(min(epoch, numel(opts.learningRate)));
+    if numel(opts.gpus) == 1
+        net = vl_simplenn_move(net, 'gpu') ;
+    end
+    
+    %-------------------------------------------------------------------------
+    % generate training data
+    %-------------------------------------------------------------------------
+    
+    if  mod(epoch,10)~=1 && isfield(imdb,'set') ~= 0
+        
+    else
+        clear imdb;
+        [imdb] = generatepatches;
+    end
+    opts.train = find(imdb.set==1);
+    
+    state.train  = opts.train(randperm(numel(opts.train))) ; % shuffle
+    [net, state] = process_epoch(net, state, imdb, opts, 'train');
+    %net.layers{end}.class =[];
+    net          = vl_simplenn_move(net, 'cpu');
+    % save current model
+    save(modelPath(epoch), 'net')
+    
+end
+
+
+function  [net, state] = process_epoch(net, state, imdb, opts, mode)
+if strcmp(mode,'train')
+    % solver: Adam
+    for i = 1:numel(net.layers)
+        if isfield(net.layers{i}, 'weights')
+            for j = 1:numel(net.layers{i}.weights)
+                state.layers{i}.t{j} = 0;
+                state.layers{i}.m{j} = 0;
+                state.layers{i}.v{j} = 0;
+            end
+        end
+    end
+end
+
+subset = state.(mode) ;
+num = 0 ;
+res = [];
+for t=1:opts.batchSize:numel(subset)
+    for s=1:opts.numSubBatches
+        
+        % get this image batch
+        batchStart = t + (s-1);
+        batchEnd = min(t+opts.batchSize-1, numel(subset));
+        batch = subset(batchStart : opts.numSubBatches : batchEnd) ;
+        num = num + numel(batch) ;
+        if numel(batch) == 0, continue ; end
+        
+        [inputs,labels] = state.getBatch(imdb, batch) ;
+        
+        if numel(opts.gpus) >= 1
+            inputs = gpuArray(inputs);
+            labels = gpuArray(labels);
+        end
+        
+        if strcmp(mode, 'train')
+            dzdy     = single(1);
+            evalMode = 'normal';% forward and backward
+        else
+            dzdy     = [] ;
+            evalMode = 'test';  % forward only
+        end
+        
+        net.layers{end}.class = labels ;
+        res = vl_simplenn(net, inputs, dzdy, res, ...
+            'accumulate', s ~= 1, ...
+            'mode', evalMode, ...
+            'conserveMemory', opts.conserveMemory, ...
+            'backPropDepth', opts.backPropDepth, ...
+            'cudnn', opts.cudnn) ;
+    end
+    
+    if strcmp(mode, 'train')
+        [state, net] = params_updates(state, net, res, opts, opts.batchSize) ;
+    end
+    
+    lossL2 = gather(res(end).x) ;
+    
+    %--------add your code here------------------------
+    
+    %--------------------------------------------------
+    
+    fprintf('%s: epoch %02d : %3d/%3d: loss: %4.4f \n', mode, state.epoch,  ...
+        fix((t-1)/opts.batchSize)+1, ceil(numel(subset)/opts.batchSize),lossL2) ;
+   % fprintf('loss: %4.4f \n', lossL2) ;
+    
+end
+
+
+function [state, net] = params_updates(state, net, res, opts, batchSize)
+
+% solver: Adam
+for l=numel(net.layers):-1:1
+    for j=1:numel(res(l).dzdw)
+        
+        if j == 3 && strcmp(net.layers{l}.type, 'bnorm')
+            
+            % special case for learning bnorm moments
+            thisLR = net.layers{l}.learningRate(j);
+            net.layers{l}.weights{j} = vl_taccum(...
+                1 - thisLR, ...
+                net.layers{l}.weights{j}, ...
+                thisLR / batchSize, ...
+                res(l).dzdw{j}) ;
+            
+        else
+            
+            %  if   j == 1 && strcmp(net.layers{l}.type, 'bnorm')
+            %         c = net.layers{l}.weights{j};
+            %         net.layers{l}.weights{j} = clipping(c,mean(abs(c))/2);
+            %  end
+            
+            thisLR = state.learningRate * net.layers{l}.learningRate(j);
+            state.layers{l}.t{j} = state.layers{l}.t{j} + 1;
+            t = state.layers{l}.t{j};
+            alpha = thisLR;
+            lr = alpha * sqrt(1 - opts.beta2^t) / (1 - opts.beta1^t);
+            
+            state.layers{l}.m{j} = state.layers{l}.m{j} + (1 - opts.beta1) .* (res(l).dzdw{j} - state.layers{l}.m{j});
+            state.layers{l}.v{j} = state.layers{l}.v{j} + (1 - opts.beta2) .* (res(l).dzdw{j} .* res(l).dzdw{j} - state.layers{l}.v{j});
+            
+            % weight decay
+            net.layers{l}.weights{j} = net.layers{l}.weights{j} -  thisLR * opts.weightDecay * net.layers{l}.weightDecay(j) * net.layers{l}.weights{j};
+            
+            % update weights
+            net.layers{l}.weights{j} = net.layers{l}.weights{j} - lr * state.layers{l}.m{j} ./ (sqrt(state.layers{l}.v{j}) + opts.epsilon) ;
+            
+            
+            %--------add your own code to update the parameters here-------
+            
+            if rand > 0.99
+                A = net.layers{l}.weights{j};
+                if numel(A)>=3*3*64
+                    A = reshape(A,[size(A,1)*size(A,2)*size(A,3),size(A,4),1,1]);
+                    if size(A,1)> size(A,2)
+                        [U,S,V] = svd(A,0);
+                    else
+                        [U,S,V] = svd(A,'econ');
+                    end
+                    S1 =smallClipping2(diag(S),1.1,0.9);
+                    A = U*diag(S1)*V';
+                    A = reshape(A,size(net.layers{l}.weights{j}));
+                    net.layers{l}.weights{j} = A;
+                end
+            end
+            
+            %--------------------------------------------------------------
+            
+        end
+    end
+end
+%end
+
+
+function epoch = findLastCheckpoint(modelDir,modelName)
+list = dir(fullfile(modelDir, [modelName,'-epoch-*.mat'])) ;
+tokens = regexp({list.name}, [modelName,'-epoch-([\d]+).mat'], 'tokens') ;
+epoch = cellfun(@(x) sscanf(x{1}{1}, '%d'), tokens) ;
+epoch = max([epoch 0]) ;
+
+
+function A = smallClipping(A, theta)
+A(A>theta)  = A(A>theta) -0.000001;
+A(A<-theta) = A(A<-theta)+0.000001;
+
+
+function A = smallClipping2(A, theta1,theta2)
+A(A>theta1)  = A(A>theta1)-0.00001;
+A(A<theta2)  = A(A<theta2)+0.00001;
+
+
+function fn = getBatch
+fn = @(x,y) getSimpleNNBatch(x,y);
+
+
+
+function [inputs,labels] = getSimpleNNBatch(imdb, batch)
+
+global sigmas;
+K = randi(8);
+labels = imdb.HRlabels(:,:,:,batch);
+labels = data_augmentation(labels,K);
+
+sigma_max = 75;
+sigmas = (rand(1,size(labels,4))*sigma_max)/255;
+%sigmas   = round(7*chi2rnd(3,[1,size(labels,4)]))/255;
+inputs = labels + bsxfun(@times,randn(size(labels)), reshape(sigmas,[1,1,1,size(labels,4)]));
+
+
+
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/Cal_PSNRSSIM.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/Cal_PSNRSSIM.m
new file mode 100644
index 0000000000000000000000000000000000000000..7307e739e2d1c220e33a61ad56de84b5b8719153
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/Cal_PSNRSSIM.m
@@ -0,0 +1,221 @@
+function [psnr_cur, ssim_cur] = Cal_PSNRSSIM(A,B,row,col)
+
+
+[n,m,ch]=size(B);
+A = A(row+1:n-row,col+1:m-col,:);
+B = B(row+1:n-row,col+1:m-col,:);
+A=double(A); % Ground-truth
+B=double(B); %
+
+e=A(:)-B(:);
+mse=mean(e.^2);
+psnr_cur=10*log10(255^2/mse);
+
+if ch==1
+    [ssim_cur, ~] = ssim_index(A, B);
+else
+    ssim_cur = (ssim_index(A(:,:,1), B(:,:,1)) + ssim_index(A(:,:,2), B(:,:,2)) + ssim_index(A(:,:,3), B(:,:,3)))/3;
+end
+
+
+function [mssim, ssim_map] = ssim_index(img1, img2, K, window, L)
+
+%========================================================================
+%SSIM Index, Version 1.0
+%Copyright(c) 2003 Zhou Wang
+%All Rights Reserved.
+%
+%The author is with Howard Hughes Medical Institute, and Laboratory
+%for Computational Vision at Center for Neural Science and Courant
+%Institute of Mathematical Sciences, New York University.
+%
+%----------------------------------------------------------------------
+%Permission to use, copy, or modify this software and its documentation
+%for educational and research purposes only and without fee is hereby
+%granted, provided that this copyright notice and the original authors'
+%names appear on all copies and supporting documentation. This program
+%shall not be used, rewritten, or adapted as the basis of a commercial
+%software or hardware product without first obtaining permission of the
+%authors. The authors make no representations about the suitability of
+%this software for any purpose. It is provided "as is" without express
+%or implied warranty.
+%----------------------------------------------------------------------
+%
+%This is an implementation of the algorithm for calculating the
+%Structural SIMilarity (SSIM) index between two images. Please refer
+%to the following paper:
+%
+%Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image
+%quality assessment: From error measurement to structural similarity"
+%IEEE Transactios on Image Processing, vol. 13, no. 1, Jan. 2004.
+%
+%Kindly report any suggestions or corrections to zhouwang@ieee.org
+%
+%----------------------------------------------------------------------
+%
+%Input : (1) img1: the first image being compared
+%        (2) img2: the second image being compared
+%        (3) K: constants in the SSIM index formula (see the above
+%            reference). defualt value: K = [0.01 0.03]
+%        (4) window: local window for statistics (see the above
+%            reference). default widnow is Gaussian given by
+%            window = fspecial('gaussian', 11, 1.5);
+%        (5) L: dynamic range of the images. default: L = 255
+%
+%Output: (1) mssim: the mean SSIM index value between 2 images.
+%            If one of the images being compared is regarded as
+%            perfect quality, then mssim can be considered as the
+%            quality measure of the other image.
+%            If img1 = img2, then mssim = 1.
+%        (2) ssim_map: the SSIM index map of the test image. The map
+%            has a smaller size than the input images. The actual size:
+%            size(img1) - size(window) + 1.
+%
+%Default Usage:
+%   Given 2 test images img1 and img2, whose dynamic range is 0-255
+%
+%   [mssim ssim_map] = ssim_index(img1, img2);
+%
+%Advanced Usage:
+%   User defined parameters. For example
+%
+%   K = [0.05 0.05];
+%   window = ones(8);
+%   L = 100;
+%   [mssim ssim_map] = ssim_index(img1, img2, K, window, L);
+%
+%See the results:
+%
+%   mssim                        %Gives the mssim value
+%   imshow(max(0, ssim_map).^4)  %Shows the SSIM index map
+%
+%========================================================================
+
+
+if (nargin < 2 || nargin > 5)
+    ssim_index = -Inf;
+    ssim_map = -Inf;
+    return;
+end
+
+if (size(img1) ~= size(img2))
+    ssim_index = -Inf;
+    ssim_map = -Inf;
+    return;
+end
+
+[M N] = size(img1);
+
+if (nargin == 2)
+    if ((M < 11) || (N < 11))
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return
+    end
+    window = fspecial('gaussian', 11, 1.5);	%
+    K(1) = 0.01;								      % default settings
+    K(2) = 0.03;								      %
+    L = 255;                                  %
+end
+
+if (nargin == 3)
+    if ((M < 11) || (N < 11))
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return
+    end
+    window = fspecial('gaussian', 11, 1.5);
+    L = 255;
+    if (length(K) == 2)
+        if (K(1) < 0 || K(2) < 0)
+            ssim_index = -Inf;
+            ssim_map = -Inf;
+            return;
+        end
+    else
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return;
+    end
+end
+
+if (nargin == 4)
+    [H W] = size(window);
+    if ((H*W) < 4 || (H > M) || (W > N))
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return
+    end
+    L = 255;
+    if (length(K) == 2)
+        if (K(1) < 0 || K(2) < 0)
+            ssim_index = -Inf;
+            ssim_map = -Inf;
+            return;
+        end
+    else
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return;
+    end
+end
+
+if (nargin == 5)
+    [H W] = size(window);
+    if ((H*W) < 4 || (H > M) || (W > N))
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return
+    end
+    if (length(K) == 2)
+        if (K(1) < 0 || K(2) < 0)
+            ssim_index = -Inf;
+            ssim_map = -Inf;
+            return;
+        end
+    else
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return;
+    end
+end
+
+C1 = (K(1)*L)^2;
+C2 = (K(2)*L)^2;
+window = window/sum(sum(window));
+img1 = double(img1);
+img2 = double(img2);
+
+mu1   = filter2(window, img1, 'valid');
+mu2   = filter2(window, img2, 'valid');
+mu1_sq = mu1.*mu1;
+mu2_sq = mu2.*mu2;
+mu1_mu2 = mu1.*mu2;
+sigma1_sq = filter2(window, img1.*img1, 'valid') - mu1_sq;
+sigma2_sq = filter2(window, img2.*img2, 'valid') - mu2_sq;
+sigma12 = filter2(window, img1.*img2, 'valid') - mu1_mu2;
+
+if (C1 > 0 & C2 > 0)
+    ssim_map = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))./((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2));
+else
+    numerator1 = 2*mu1_mu2 + C1;
+    numerator2 = 2*sigma12 + C2;
+    denominator1 = mu1_sq + mu2_sq + C1;
+    denominator2 = sigma1_sq + sigma2_sq + C2;
+    ssim_map = ones(size(mu1));
+    index = (denominator1.*denominator2 > 0);
+    ssim_map(index) = (numerator1(index).*numerator2(index))./(denominator1(index).*denominator2(index));
+    index = (denominator1 ~= 0) & (denominator2 == 0);
+    ssim_map(index) = numerator1(index)./denominator1(index);
+end
+
+mssim = mean2(ssim_map);
+
+return
+
+
+
+
+
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/FFDNet_grayscale_retrained.mat b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/FFDNet_grayscale_retrained.mat
new file mode 100644
index 0000000000000000000000000000000000000000..ab747a33ea9b467ba2d19d992f86d474d7b6fee5
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/FFDNet_grayscale_retrained.mat
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:245a158403abaac462a952c64668f18e41a00c0b5d63d720b35ac4c788677ea4
+size 1817925
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/Merge_Bnorm_Demo.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/Merge_Bnorm_Demo.m
new file mode 100644
index 0000000000000000000000000000000000000000..633815b0af42231cb75299cf4cf544b48a2da8cf
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/Merge_Bnorm_Demo.m
@@ -0,0 +1,12 @@
+
+
+
+
+
+load('sigma=25_Bnorm.mat');
+
+[net] = vl_simplenn_mergebnorm(net);
+
+save sigma=25 net;
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/data_augmentation.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/data_augmentation.m
new file mode 100644
index 0000000000000000000000000000000000000000..ed1f357325e31703aeca019a09e6edcb4749b965
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/data_augmentation.m
@@ -0,0 +1,36 @@
+function I = data_augmentation(I, K)
+
+
+if K == 1
+    return;
+elseif K == 2 % flipped
+    I = flipud(I);
+    return;
+elseif K == 3 % rotation 90
+    I = rot90(I,1);
+    return;
+elseif K == 4 % rotation 90 & flipped
+    I = rot90(I,1);
+    I = flipud(I);
+    return;
+elseif K == 5 % rotation 180
+    I = rot90(I,2);
+    return;
+elseif K == 6 % rotation 180 & flipped
+    I = rot90(I,2);
+    I = flipud(I);
+    return;
+elseif K == 7 % rotation 270
+    I = rot90(I,3);
+    return;
+elseif K == 8 % rotation 270 & flipped
+    I = rot90(I,3);
+    I = flipud(I);
+    return;
+end
+
+
+
+
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/modcrop.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/modcrop.m
new file mode 100644
index 0000000000000000000000000000000000000000..bfc6a3fe313b52fb0677fd2432c03ca8e550b373
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/modcrop.m
@@ -0,0 +1,12 @@
+function imgs = modcrop(imgs, modulo)
+if size(imgs,3)==1
+    sz = size(imgs);
+    sz = sz - mod(sz, modulo);
+    imgs = imgs(1:sz(1), 1:sz(2));
+else
+    tmpsz = size(imgs);
+    sz = tmpsz(1:2);
+    sz = sz - mod(sz, modulo);
+    imgs = imgs(1:sz(1), 1:sz(2),:);
+end
+
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/shave.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/shave.m
new file mode 100644
index 0000000000000000000000000000000000000000..9790fe39317438a2beb820cdc6c27d2b15b20d3c
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/shave.m
@@ -0,0 +1,3 @@
+function I = shave(I, border)
+I = I(1+border(1):end-border(1), ...
+      1+border(2):end-border(2), :, :);
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/vl_ffdnet_concise.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/vl_ffdnet_concise.m
new file mode 100644
index 0000000000000000000000000000000000000000..1798e78f8865c3b6bd8f08e3448d72215914229a
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/vl_ffdnet_concise.m
@@ -0,0 +1,38 @@
+function res = vl_ffdnet_concise(net, x)
+
+global sigmas;
+n = numel(net.layers);
+res = struct('x', cell(1,n+1));
+res(1).x = x ;
+cudnn = {'CuDNN'} ;
+%cudnn = {'NoCuDNN'} ;
+
+for i=1:n
+    l = net.layers{i} ;
+    switch l.type
+        case 'conv'
+            res(i+1).x = vl_nnconv(res(i).x, l.weights{1}, l.weights{2}, ...
+                'pad', l.pad, ...
+                'stride', l.stride, ...
+                'dilate', l.dilate, ...
+                l.opts{:}, ...
+                cudnn{:}) ;
+            
+        case 'concat'
+            if size(sigmas,1)~=size(res(i).x,1)
+                sigmaMap   = bsxfun(@times,ones(size(res(i).x,1),size(res(i).x,2),1,size(res(i).x,4),'single'),permute(sigmas,[3 4 1 2]));
+                res(i+1).x = cat(3,res(i).x,sigmaMap);
+            else
+                res(i+1).x = cat(3,res(i).x,sigmaMap);
+            end
+
+        case 'SubP'
+            res(i+1).x = vl_nnSubP(res(i).x, [],'scale',l.scale);
+  
+        case 'relu'
+            res(i+1).x = max(res(i).x,0) ;
+    end
+        res(i).x = [] ;
+end
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/vl_ffdnet_matlab.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/vl_ffdnet_matlab.m
new file mode 100644
index 0000000000000000000000000000000000000000..cd236cfd79ca076469675dcf6314d51751694f4b
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/vl_ffdnet_matlab.m
@@ -0,0 +1,42 @@
+function res = vl_ffdnet_matlab(net, input)
+
+%% If you did not install the matconvnet package, you can use this for testing.
+
+global sigmas;
+n = numel(net.layers);
+res = struct('x', cell(1,n+1));
+res(1).x = input;
+
+for i = 1 : n
+    l = net.layers{i};
+    switch l.type
+        
+        case 'conv'
+            disp(['Processing ... ',int2str(i),'/',int2str(n)]);
+            for noutmaps = 1 : size(l.weights{1},4)
+                z = zeros(size(res(i).x,1),size(res(i).x,2),'single');
+                for ninmaps = 1 : size(res(i).x,3)
+                    z = z + convn(res(i).x(:,:,ninmaps), rot90(l.weights{1}(:,:,ninmaps,noutmaps),2),'same'); % 180 degree rotation for kernel
+                end
+                res(i+1).x(:,:,noutmaps) = z + l.weights{2}(noutmaps);
+            end
+            
+        case 'relu'
+            res(i+1).x = max(res(i).x,0);
+            
+        case 'concat'
+            if size(sigmas,1)~=size(res(i).x,1)
+                sigmaMap   = bsxfun(@times,ones(size(res(i).x,1),size(res(i).x,2),1,size(res(i).x,4),'single'),permute(sigmas,[3 4 1 2]));
+                res(i+1).x = cat(3,res(i).x,sigmaMap);
+            else
+                res(i+1).x = cat(3,res(i).x,sigmaMap);
+            end
+            
+        case 'SubP'
+            res(i+1).x = vl_nnSubP(res(i).x, [],'scale',l.scale);
+            
+    end
+    res(i).x = [];
+end
+
+end
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/vl_nnSubP.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/vl_nnSubP.m
new file mode 100644
index 0000000000000000000000000000000000000000..acdc9bce80ce7b03d43fe483ad8ed7958cdb4341
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/vl_nnSubP.m
@@ -0,0 +1,78 @@
+function y = vl_nnSubP(x, dzdy, varargin)
+
+
+opts.scale = 2;
+opts = vl_argparse(opts, varargin, 'nonrecursive') ;
+scale = opts.scale;
+
+if scale > 1 % many (small)---> one (big) e.g., 100X100X256X128  ----> 200X200X64*128
+    scale2 = scale^2;
+    if nargin <= 1 || isempty(dzdy)
+        [hei,wid,channelI,bsize] = size(x);
+        channelO = channelI/scale2;
+        channelI_start = 0;
+        idx1 = scale * (1:hei);
+        idx2 = scale * (1:wid);
+        y = zeros([scale*hei, scale*wid, channelO, bsize],'like',x);
+        for nchannelO = 1:channelO
+            for i = 1:scale2
+                a = mod(i-1, scale) + 1;
+                b = floor((i-1)/scale) + 1;
+                y(idx1+a-scale, idx2+b-scale, nchannelO, :) = x(:,:,channelI_start+i,:);
+            end
+            channelI_start = scale2 + channelI_start;
+        end
+    else
+        [hei,wid,channelO,bsize] = size(dzdy);
+        idx1 = 1:scale:hei;
+        idx2 = 1:scale:wid;
+        channelI = channelO*scale2;
+        channelI_start = 0;
+        y = zeros([hei/scale, wid/scale, channelI, bsize],'like',x);
+        for  nchannelO = 1:channelO
+            for i = 1:scale^2
+                a = mod(i-1, scale) + 1;
+                b = floor((i-1)/scale) + 1;
+                y(:, :, channelI_start+i, :) = dzdy(idx1+a-1, idx2+b-1,nchannelO,:);
+            end
+            channelI_start = scale2 + channelI_start;
+        end
+    end
+    
+else %  one (big) ---> many (small)  e.g., 200X200X64*128--->100X100X256X128
+    
+    scale = round(1/scale);
+    scale2 = scale^2;
+    if nargin <= 1 || isempty(dzdy)
+        [hei,wid,channelO,bsize] = size(x);
+        idx1 = 1:scale:hei;
+        idx2 = 1:scale:wid;
+        channelI = channelO*scale2;
+        channelI_start = 0;
+        y = zeros([hei/scale, wid/scale, channelI, bsize],'like',x);
+        for  nchannelO = 1:channelO
+            for i = 1:scale^2
+                a = mod(i-1, scale) + 1;
+                b = floor((i-1)/scale) + 1;
+                y(:, :, channelI_start+i, :) = x(idx1+a-1, idx2+b-1,nchannelO,:);
+            end
+            channelI_start = scale2 + channelI_start;
+        end
+    else
+        [hei,wid,channelI,bsize] = size(dzdy);
+        channelO = channelI/scale2;
+        channelI_start = 0;
+        idx1 = scale * (1:hei);
+        idx2 = scale * (1:wid);
+        y = zeros([scale*hei, scale*wid, channelO, bsize],'like',x);
+        for nchannelO = 1:channelO
+            for i = 1:scale2
+                a = mod(i-1, scale) + 1;
+                b = floor((i-1)/scale) + 1;
+                y(idx1+a-scale, idx2+b-scale, nchannelO, :) = dzdy(:,:,channelI_start+i,:);
+            end
+            channelI_start = scale2 + channelI_start;
+        end
+    end
+end
+
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/vl_randnm.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/vl_randnm.m
new file mode 100644
index 0000000000000000000000000000000000000000..593a7baaf858ad68c0fe712be99865e5a686d9d8
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/vl_randnm.m
@@ -0,0 +1,14 @@
+function [noise,sigma6] = vl_randnm(w, h, Sigma)
+
+% generate noise
+mu     =  [0,0,0];
+noise  =  mvnrnd(mu,Sigma,w*h);
+noise  =  reshape(noise,[w,h,3]);
+
+% extract 6 parameters
+sigma  =  sqrt(Sigma);
+sigma  =  sigma(:);
+sigma6 =  sigma([1,4,5,7,8,9]);
+
+end
+
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/vl_simplenn_mergebnorm.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/vl_simplenn_mergebnorm.m
new file mode 100644
index 0000000000000000000000000000000000000000..6142cb31a57cf1d6ecc691916531e2451ab2c691
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/utilities/vl_simplenn_mergebnorm.m
@@ -0,0 +1,36 @@
+function [net1] = vl_simplenn_mergebnorm(net)
+
+%% merge bnorm parameters into adjacent Conv layer
+
+for i = 1:numel(net.layers)
+    if strcmp(net.layers{i}.type, 'conv')
+        net.layers{i}.weightDecay(2) = 1;
+    end
+end
+
+for i = 1:numel(net.layers)
+    if strcmp(net.layers{i}.type, 'bnorm')
+        ws = net.layers{i}.weights{1};
+        bs = net.layers{i}.weights{2};
+        mu_sigmas = net.layers{i}.weights{3};
+        for j = 1:numel(ws)
+            net.layers{i-1}.weights{1}(:,:,:,j) =single(double(net.layers{i-1}.weights{1}(:,:,:,j))*double(ws(j))/(double(mu_sigmas(j,2))));
+            net.layers{i-1}.weights{2}(j) =single(double(bs(j)) - double(ws(j))*double(mu_sigmas(j,1))/(double(mu_sigmas(j,2))));
+        end
+        net.layers{i-1}.learningRate(2) = 1;
+    end
+end
+
+net1 = net;
+net1.layers = {};
+net1 = rmfield(net1,'meta');
+for i = 1:numel(net.layers)
+    if ~strcmp(net.layers{i}.type, 'bnorm')
+        net1.layers{end+1} = net.layers{i};
+    end
+end
+
+net1.layers = net1.layers(1:end-1);
+
+
+end
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/vl_nnloss.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/vl_nnloss.m
new file mode 100644
index 0000000000000000000000000000000000000000..cb2219ef5a38cc913a8f38abec72575d20b6b497
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/vl_nnloss.m
@@ -0,0 +1,47 @@
+function Y = vl_nnloss(X,c,dzdy,varargin)
+
+% --------------------------------------------------------------------
+% pixel-level L2 loss
+% --------------------------------------------------------------------
+
+d = X-c;
+if nargin <= 2 || isempty(dzdy)
+    t = (d.^2)/2;
+    Y = sum(t(:))/size(X,4); 
+else
+    Y = d;
+end
+
+% --------------------------------------------------------------------
+
+% if nargin <= 2 || isempty(dzdy)
+%     t = ((X-c).^2)/2;
+%     Y = sum(t(:))/size(X,4); % reconstruction error per sample;
+% else
+%     Y = sign(X - c);
+% end
+
+% --------------fine-tune---------------------------------------------
+
+% global sigmas;
+% d = X-c;
+% w = min(200,1./((sigmas/75*255).^1.2));
+% if nargin <= 2 || isempty(dzdy)
+%     t = (d.^2)/2;
+%     Y = sum(t(:))/size(X,4);
+% else
+%     Y = bsxfun(@times,d,reshape(w,[1,1,1,size(X,4)]));
+% end
+
+% --------------------------------------------------------------------
+
+% eps = 1e-5;
+% d = X - c;
+% e = sqrt(d.^2 + eps);
+% 
+% if nargin <= 2 || isempty(dzdy)
+%     t = (d.^2)/2;
+%     Y = sum(t(:))/size(X,4); 
+% else
+%     Y = d ./ e;
+% end
diff --git a/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/vl_simplenn.m b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/vl_simplenn.m
new file mode 100644
index 0000000000000000000000000000000000000000..147314706d3dabdae4590dfa84ead2bcf39de7e6
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/TrainingCodes/FFDNet_TrainingCodes_v1.0/vl_simplenn.m
@@ -0,0 +1,326 @@
+function res = vl_simplenn(net, x, dzdy, res, varargin)
+%VL_SIMPLENN  Evaluate a SimpleNN network.
+%   RES = VL_SIMPLENN(NET, X) evaluates the convnet NET on data X.
+%   RES = VL_SIMPLENN(NET, X, DZDY) evaluates the convnent NET and its
+%   derivative on data X and output derivative DZDY (foward+bacwkard pass).
+%   RES = VL_SIMPLENN(NET, X, [], RES) evaluates the NET on X reusing the
+%   structure RES.
+%   RES = VL_SIMPLENN(NET, X, DZDY, RES) evaluates the NET on X and its
+%   derivatives reusing the structure RES.
+% Copyright (C) 2014-15 Andrea Vedaldi.
+% All rights reserved.
+%
+% This file is part of the VLFeat library and is made available under
+% the terms of the BSD license (see the COPYING file).
+global sigmas;
+opts.conserveMemory = false ;
+opts.sync = false ;
+opts.mode = 'normal' ;
+opts.accumulate = false ;
+opts.cudnn = true ;
+opts.backPropDepth = +inf ;
+opts.skipForward = false ;
+opts.parameterServer = [] ;
+opts.holdOn = false ;
+opts = vl_argparse(opts, varargin);
+
+n = numel(net.layers) ;
+assert(opts.backPropDepth > 0, 'Invalid `backPropDepth` value (!>0)');
+backPropLim = max(n - opts.backPropDepth + 1, 1);
+
+if (nargin <= 2) || isempty(dzdy)
+    doder = false ;
+    if opts.skipForward
+        error('simplenn:skipForwardNoBackwPass', ...
+            '`skipForward` valid only when backward pass is computed.');
+    end
+else
+    doder = true ;
+end
+
+if opts.cudnn
+    cudnn = {'CuDNN'} ;
+    bnormCudnn = {'NoCuDNN'} ; % ours seems slighty faster
+else
+    cudnn = {'NoCuDNN'} ;
+    bnormCudnn = {'NoCuDNN'} ;
+end
+
+switch lower(opts.mode)
+    case 'normal'
+        testMode = false ;
+    case 'test'
+        testMode = true ;
+    otherwise
+        error('Unknown mode ''%s''.', opts. mode) ;
+end
+
+gpuMode = isa(x, 'gpuArray') ;
+
+if nargin <= 3 || isempty(res)
+    if opts.skipForward
+        error('simplenn:skipForwardEmptyRes', ...
+            'RES structure must be provided for `skipForward`.');
+    end
+    res = struct(...
+        'x', cell(1,n+1), ...
+        'dzdx', cell(1,n+1), ...
+        'dzdw', cell(1,n+1), ...
+        'aux', cell(1,n+1), ...
+        'stats', cell(1,n+1), ...
+        'time', num2cell(zeros(1,n+1)), ...
+        'backwardTime', num2cell(zeros(1,n+1))) ;
+end
+
+if ~opts.skipForward
+    res(1).x = x ;
+end
+
+% -------------------------------------------------------------------------
+%                                                              Forward pass
+% -------------------------------------------------------------------------
+
+for i=1:n
+    if opts.skipForward, break; end;
+    l = net.layers{i} ;
+    res(i).time = tic ;
+    switch l.type
+        case 'conv'
+            res(i+1).x = vl_nnconv(res(i).x, l.weights{1}, l.weights{2}, ...
+                'pad', l.pad, ...
+                'stride', l.stride, ...
+                'dilate', l.dilate, ...
+                l.opts{:}, ...
+                cudnn{:}) ;
+        case 'concat'
+                sigmaMap   = bsxfun(@times,ones(size(res(i).x,1),size(res(i).x,2),1,size(res(i).x,4),'single'),permute(sigmas,[3 4 1 2]));
+                res(i+1).x = vl_nnconcat({res(i).x,sigmaMap});
+            
+        case 'convt'
+            res(i+1).x = vl_nnconvt(res(i).x, l.weights{1}, l.weights{2}, ...
+                'crop', l.crop, ...
+                'upsample', l.upsample, ...
+                'numGroups', l.numGroups, ...
+                l.opts{:}, ...
+                cudnn{:}) ;
+            
+        case 'SubP'
+            res(i+1).x = vl_nnSubP(res(i).x, [],'scale',l.scale);
+      
+        case 'pool'
+            res(i+1).x = vl_nnpool(res(i).x, l.pool, ...
+                'pad', l.pad, 'stride', l.stride, ...
+                'method', l.method, ...
+                l.opts{:}, ...
+                cudnn{:}) ;
+            
+        case {'normalize', 'lrn'}
+            res(i+1).x = vl_nnnormalize(res(i).x, l.param) ;
+            
+        case 'softmax'
+            res(i+1).x = vl_nnsoftmax(res(i).x) ;
+            
+        case 'loss'
+            res(i+1).x = vl_nnloss(res(i).x, l.class) ;
+            
+        case 'softmaxloss'
+            res(i+1).x = vl_nnsoftmaxloss(res(i).x, l.class) ;
+            
+        case 'relu'
+            if l.leak > 0, leak = {'leak', l.leak} ; else leak = {} ; end
+            res(i+1).x = vl_nnrelu(res(i).x,[],leak{:}) ;
+            
+        case 'sigmoid'
+            res(i+1).x = vl_nnsigmoid(res(i).x) ;
+            
+        case 'noffset'
+            res(i+1).x = vl_nnnoffset(res(i).x, l.param) ;
+            
+        case 'spnorm'
+            res(i+1).x = vl_nnspnorm(res(i).x, l.param) ;
+            
+        case 'dropout'
+            if testMode
+                res(i+1).x = res(i).x ;
+            else
+                [res(i+1).x, res(i+1).aux] = vl_nndropout(res(i).x, 'rate', l.rate) ;
+            end
+            
+        case 'bnorm'
+            if testMode
+                res(i+1).x = vl_nnbnorm(res(i).x, l.weights{1}, l.weights{2}, ...
+                    'moments', l.weights{3}, ...
+                    'epsilon', l.epsilon, ...
+                    bnormCudnn{:}) ;
+            else
+                res(i+1).x = vl_nnbnorm(res(i).x, l.weights{1}, l.weights{2}, ...
+                    'epsilon', l.epsilon, ...
+                    bnormCudnn{:}) ;
+            end
+            
+        case 'pdist'
+            res(i+1).x = vl_nnpdist(res(i).x, l.class, l.p, ...
+                'noRoot', l.noRoot, ...
+                'epsilon', l.epsilon, ...
+                'aggregate', l.aggregate, ...
+                'instanceWeights', l.instanceWeights) ;
+            
+        case 'custom'
+            res(i+1) = l.forward(l, res(i), res(i+1)) ;
+            
+        otherwise
+            error('Unknown layer type ''%s''.', l.type) ;
+    end
+    
+    % optionally forget intermediate results
+    needsBProp = doder && i >= backPropLim;
+    forget = opts.conserveMemory && ~needsBProp ;
+    if i > 1
+        lp = net.layers{i-1} ;
+        % forget RELU input, even for BPROP
+        forget = forget && (~needsBProp || (strcmp(l.type, 'relu') && ~lp.precious)) ;
+        forget = forget && ~(strcmp(lp.type, 'loss') || strcmp(lp.type, 'softmaxloss')) ;
+        forget = forget && ~lp.precious ;
+    end
+    if forget
+        res(i).x = [] ;
+    end
+    
+    if gpuMode && opts.sync
+        wait(gpuDevice) ;
+    end
+    res(i).time = toc(res(i).time) ;
+end
+
+% -------------------------------------------------------------------------
+%                                                             Backward pass
+% -------------------------------------------------------------------------
+
+if doder
+    res(n+1).dzdx = dzdy ;
+    for i=n:-1:backPropLim
+        l = net.layers{i} ;
+        res(i).backwardTime = tic ;
+        switch l.type
+            
+            case 'conv'
+                [res(i).dzdx, dzdw{1}, dzdw{2}] = ...
+                    vl_nnconv(res(i).x, l.weights{1}, l.weights{2}, res(i+1).dzdx, ...
+                    'pad', l.pad, ...
+                    'stride', l.stride, ...
+                    'dilate', l.dilate, ...
+                    l.opts{:}, ...
+                    cudnn{:}) ;
+                
+            case 'convt'
+                [res(i).dzdx, dzdw{1}, dzdw{2}] = ...
+                    vl_nnconvt(res(i).x, l.weights{1}, l.weights{2}, res(i+1).dzdx, ...
+                    'crop', l.crop, ...
+                    'upsample', l.upsample, ...
+                    'numGroups', l.numGroups, ...
+                    l.opts{:}, ...
+                    cudnn{:}) ;
+                
+            case 'pool'
+                res(i).dzdx = vl_nnpool(res(i).x, l.pool, res(i+1).dzdx, ...
+                    'pad', l.pad, 'stride', l.stride, ...
+                    'method', l.method, ...
+                    l.opts{:}, ...
+                    cudnn{:}) ;
+                
+            case {'normalize', 'lrn'}
+                res(i).dzdx = vl_nnnormalize(res(i).x, l.param, res(i+1).dzdx) ;
+                
+            case 'SubP'
+                res(i).dzdx = vl_nnSubP(res(i).x, res(i+1).dzdx,'scale',l.scale);
+                
+            case 'softmax'
+                res(i).dzdx = vl_nnsoftmax(res(i).x, res(i+1).dzdx) ;
+                
+            case 'loss'
+                res(i).dzdx = vl_nnloss(res(i).x, l.class, res(i+1).dzdx) ;
+                
+            case 'softmaxloss'
+                res(i).dzdx = vl_nnsoftmaxloss(res(i).x, l.class, res(i+1).dzdx) ;
+                
+            case 'relu'
+                if l.leak > 0, leak = {'leak', l.leak} ; else leak = {} ; end
+                if ~isempty(res(i).x)
+                    res(i).dzdx = vl_nnrelu(res(i).x, res(i+1).dzdx, leak{:}) ;
+                else
+                    % if res(i).x is empty, it has been optimized away, so we use this
+                    % hack (which works only for ReLU):
+                    res(i).dzdx = vl_nnrelu(res(i+1).x, res(i+1).dzdx, leak{:}) ;
+                end
+                
+            case 'sigmoid'
+                res(i).dzdx = vl_nnsigmoid(res(i).x, res(i+1).dzdx) ;
+                
+            case 'noffset'
+                res(i).dzdx = vl_nnnoffset(res(i).x, l.param, res(i+1).dzdx) ;
+                
+            case 'spnorm'
+                res(i).dzdx = vl_nnspnorm(res(i).x, l.param, res(i+1).dzdx) ;
+                
+            case 'dropout'
+                if testMode
+                    res(i).dzdx = res(i+1).dzdx ;
+                else
+                    res(i).dzdx = vl_nndropout(res(i).x, res(i+1).dzdx, ...
+                        'mask', res(i+1).aux) ;
+                end
+                
+            case 'bnorm'
+                [res(i).dzdx, dzdw{1}, dzdw{2}, dzdw{3}] = ...
+                    vl_nnbnorm(res(i).x, l.weights{1}, l.weights{2}, res(i+1).dzdx, ...
+                    'epsilon', l.epsilon, ...
+                    bnormCudnn{:}) ;
+                % multiply the moments update by the number of images in the batch
+                % this is required to make the update additive for subbatches
+                % and will eventually be normalized away
+                dzdw{3} = dzdw{3} * size(res(i).x,4) ;
+                
+            case 'pdist'
+                res(i).dzdx = vl_nnpdist(res(i).x, l.class, ...
+                    l.p, res(i+1).dzdx, ...
+                    'noRoot', l.noRoot, ...
+                    'epsilon', l.epsilon, ...
+                    'aggregate', l.aggregate, ...
+                    'instanceWeights', l.instanceWeights) ;
+                
+            case 'custom'
+                res(i) = l.backward(l, res(i), res(i+1)) ;
+                
+        end % layers
+        
+        switch l.type
+            case {'conv', 'convt', 'bnorm'}
+                if ~opts.accumulate
+                    res(i).dzdw = dzdw ;
+                else
+                    for j=1:numel(dzdw)
+                        res(i).dzdw{j} = res(i).dzdw{j} + dzdw{j} ;
+                    end
+                end
+                dzdw = [] ;
+                if ~isempty(opts.parameterServer) && ~opts.holdOn
+                    for j = 1:numel(res(i).dzdw)
+                        opts.parameterServer.push(sprintf('l%d_%d',i,j),res(i).dzdw{j}) ;
+                        res(i).dzdw{j} = [] ;
+                    end
+                end
+        end
+        if opts.conserveMemory && ~net.layers{i}.precious && i ~= n
+            res(i+1).dzdx = [] ;
+            res(i+1).x = [] ;
+        end
+        if gpuMode && opts.sync
+            wait(gpuDevice) ;
+        end
+        res(i).backwardTime = toc(res(i).backwardTime) ;
+    end
+    if i > 1 && i == backPropLim && opts.conserveMemory && ~net.layers{i}.precious
+        res(i).dzdx = [] ;
+        res(i).x = [] ;
+    end
+end
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+
+
+[n,m,ch]=size(B);
+A = A(row+1:n-row,col+1:m-col,:);
+B = B(row+1:n-row,col+1:m-col,:);
+A=double(A); % Ground-truth
+B=double(B); %
+
+e=A(:)-B(:);
+mse=mean(e.^2);
+psnr_cur=10*log10(255^2/mse);
+
+if ch==1
+    [ssim_cur, ~] = ssim_index(A, B);
+else
+    ssim_cur = (ssim_index(A(:,:,1), B(:,:,1)) + ssim_index(A(:,:,2), B(:,:,2)) + ssim_index(A(:,:,3), B(:,:,3)))/3;
+end
+
+
+function [mssim, ssim_map] = ssim_index(img1, img2, K, window, L)
+
+%========================================================================
+%SSIM Index, Version 1.0
+%Copyright(c) 2003 Zhou Wang
+%All Rights Reserved.
+%
+%The author is with Howard Hughes Medical Institute, and Laboratory
+%for Computational Vision at Center for Neural Science and Courant
+%Institute of Mathematical Sciences, New York University.
+%
+%----------------------------------------------------------------------
+%Permission to use, copy, or modify this software and its documentation
+%for educational and research purposes only and without fee is hereby
+%granted, provided that this copyright notice and the original authors'
+%names appear on all copies and supporting documentation. This program
+%shall not be used, rewritten, or adapted as the basis of a commercial
+%software or hardware product without first obtaining permission of the
+%authors. The authors make no representations about the suitability of
+%this software for any purpose. It is provided "as is" without express
+%or implied warranty.
+%----------------------------------------------------------------------
+%
+%This is an implementation of the algorithm for calculating the
+%Structural SIMilarity (SSIM) index between two images. Please refer
+%to the following paper:
+%
+%Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image
+%quality assessment: From error measurement to structural similarity"
+%IEEE Transactios on Image Processing, vol. 13, no. 1, Jan. 2004.
+%
+%Kindly report any suggestions or corrections to zhouwang@ieee.org
+%
+%----------------------------------------------------------------------
+%
+%Input : (1) img1: the first image being compared
+%        (2) img2: the second image being compared
+%        (3) K: constants in the SSIM index formula (see the above
+%            reference). defualt value: K = [0.01 0.03]
+%        (4) window: local window for statistics (see the above
+%            reference). default widnow is Gaussian given by
+%            window = fspecial('gaussian', 11, 1.5);
+%        (5) L: dynamic range of the images. default: L = 255
+%
+%Output: (1) mssim: the mean SSIM index value between 2 images.
+%            If one of the images being compared is regarded as
+%            perfect quality, then mssim can be considered as the
+%            quality measure of the other image.
+%            If img1 = img2, then mssim = 1.
+%        (2) ssim_map: the SSIM index map of the test image. The map
+%            has a smaller size than the input images. The actual size:
+%            size(img1) - size(window) + 1.
+%
+%Default Usage:
+%   Given 2 test images img1 and img2, whose dynamic range is 0-255
+%
+%   [mssim ssim_map] = ssim_index(img1, img2);
+%
+%Advanced Usage:
+%   User defined parameters. For example
+%
+%   K = [0.05 0.05];
+%   window = ones(8);
+%   L = 100;
+%   [mssim ssim_map] = ssim_index(img1, img2, K, window, L);
+%
+%See the results:
+%
+%   mssim                        %Gives the mssim value
+%   imshow(max(0, ssim_map).^4)  %Shows the SSIM index map
+%
+%========================================================================
+
+
+if (nargin < 2 || nargin > 5)
+    ssim_index = -Inf;
+    ssim_map = -Inf;
+    return;
+end
+
+if (size(img1) ~= size(img2))
+    ssim_index = -Inf;
+    ssim_map = -Inf;
+    return;
+end
+
+[M N] = size(img1);
+
+if (nargin == 2)
+    if ((M < 11) || (N < 11))
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return
+    end
+    window = fspecial('gaussian', 11, 1.5);	%
+    K(1) = 0.01;								      % default settings
+    K(2) = 0.03;								      %
+    L = 255;                                  %
+end
+
+if (nargin == 3)
+    if ((M < 11) || (N < 11))
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return
+    end
+    window = fspecial('gaussian', 11, 1.5);
+    L = 255;
+    if (length(K) == 2)
+        if (K(1) < 0 || K(2) < 0)
+            ssim_index = -Inf;
+            ssim_map = -Inf;
+            return;
+        end
+    else
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return;
+    end
+end
+
+if (nargin == 4)
+    [H W] = size(window);
+    if ((H*W) < 4 || (H > M) || (W > N))
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return
+    end
+    L = 255;
+    if (length(K) == 2)
+        if (K(1) < 0 || K(2) < 0)
+            ssim_index = -Inf;
+            ssim_map = -Inf;
+            return;
+        end
+    else
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return;
+    end
+end
+
+if (nargin == 5)
+    [H W] = size(window);
+    if ((H*W) < 4 || (H > M) || (W > N))
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return
+    end
+    if (length(K) == 2)
+        if (K(1) < 0 || K(2) < 0)
+            ssim_index = -Inf;
+            ssim_map = -Inf;
+            return;
+        end
+    else
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return;
+    end
+end
+
+C1 = (K(1)*L)^2;
+C2 = (K(2)*L)^2;
+window = window/sum(sum(window));
+img1 = double(img1);
+img2 = double(img2);
+
+mu1   = filter2(window, img1, 'valid');
+mu2   = filter2(window, img2, 'valid');
+mu1_sq = mu1.*mu1;
+mu2_sq = mu2.*mu2;
+mu1_mu2 = mu1.*mu2;
+sigma1_sq = filter2(window, img1.*img1, 'valid') - mu1_sq;
+sigma2_sq = filter2(window, img2.*img2, 'valid') - mu2_sq;
+sigma12 = filter2(window, img1.*img2, 'valid') - mu1_mu2;
+
+if (C1 > 0 & C2 > 0)
+    ssim_map = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))./((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2));
+else
+    numerator1 = 2*mu1_mu2 + C1;
+    numerator2 = 2*sigma12 + C2;
+    denominator1 = mu1_sq + mu2_sq + C1;
+    denominator2 = sigma1_sq + sigma2_sq + C2;
+    ssim_map = ones(size(mu1));
+    index = (denominator1.*denominator2 > 0);
+    ssim_map(index) = (numerator1(index).*numerator2(index))./(denominator1(index).*denominator2(index));
+    index = (denominator1 ~= 0) & (denominator2 == 0);
+    ssim_map(index) = numerator1(index)./denominator1(index);
+end
+
+mssim = mean2(ssim_map);
+
+return
+
+
+
+
+
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/utilities/data_augmentation.m b/datasets/Test_Datasets/FFDNet-master/utilities/data_augmentation.m
new file mode 100644
index 0000000000000000000000000000000000000000..349c61a8bedd7b5b10e679959918359f109137f2
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/utilities/data_augmentation.m
@@ -0,0 +1,60 @@
+function image = data_augmentation(image, mode)
+
+if mode == 1
+    return;
+end
+
+if mode == 2 % flipped
+    image = flipud(image);
+    return;
+end
+
+if mode == 3 % rotation 90
+    image = rot90(image,1);
+    return;
+end
+
+if mode == 4 % rotation 90 & flipped
+    image = rot90(image,1);
+    image = flipud(image);
+    return;
+end
+
+if mode == 5 % rotation 180
+    image = rot90(image,2);
+    return;
+end
+
+if mode == 6 % rotation 180 & flipped
+    image = rot90(image,2);
+    image = flipud(image);
+    return;
+end
+
+if mode == 7 % rotation 270
+    image = rot90(image,3);
+    return;
+end
+
+if mode == 8 % rotation 270 & flipped
+    image = rot90(image,3);
+    image = flipud(image);
+    return;
+end
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/utilities/figs/05_75_75.png b/datasets/Test_Datasets/FFDNet-master/utilities/figs/05_75_75.png
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diff --git a/datasets/Test_Datasets/FFDNet-master/utilities/figs/05_75_75_PSNR_2479.png b/datasets/Test_Datasets/FFDNet-master/utilities/figs/05_75_75_PSNR_2479.png
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diff --git a/datasets/Test_Datasets/FFDNet-master/utilities/figs/102061_75_75.png b/datasets/Test_Datasets/FFDNet-master/utilities/figs/102061_75_75.png
new file mode 100644
index 0000000000000000000000000000000000000000..1f0b2c35e3f197c3aa9ac9d1c6827bef018e40f9
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/utilities/figs/102061_75_75.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f9d6b1281cd5e866c6d4608ccb7217f486c02cf3c8aee3e4a8b4fbd47c0977bd
+size 445657
diff --git a/datasets/Test_Datasets/FFDNet-master/utilities/figs/102061_75_75_PSNR_2698.png b/datasets/Test_Datasets/FFDNet-master/utilities/figs/102061_75_75_PSNR_2698.png
new file mode 100644
index 0000000000000000000000000000000000000000..b5ba6e3f599ea938ea79e9591dbdcb4f98ac64d8
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/utilities/figs/102061_75_75_PSNR_2698.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d65140dfcbaae53ca3c5ec1202945fa6b08e04587376d81e8e099d5be5dba172
+size 169136
diff --git a/datasets/Test_Datasets/FFDNet-master/utilities/figs/David_Hilbert.jpg b/datasets/Test_Datasets/FFDNet-master/utilities/figs/David_Hilbert.jpg
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diff --git a/datasets/Test_Datasets/FFDNet-master/utilities/figs/David_Hilbert_15.png b/datasets/Test_Datasets/FFDNet-master/utilities/figs/David_Hilbert_15.png
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diff --git a/datasets/Test_Datasets/FFDNet-master/utilities/figs/Frog.gif b/datasets/Test_Datasets/FFDNet-master/utilities/figs/Frog.gif
new file mode 100644
index 0000000000000000000000000000000000000000..11cd0592ce26f5913c333eb4e88b731660c17c51
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/utilities/figs/Frog.gif
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ed7ea8f745f1b05b89432be02995685691f6335601763dcd03d6f4be5ddda6b0
+size 1290151
diff --git a/datasets/Test_Datasets/FFDNet-master/utilities/modcrop.m b/datasets/Test_Datasets/FFDNet-master/utilities/modcrop.m
new file mode 100644
index 0000000000000000000000000000000000000000..bfc6a3fe313b52fb0677fd2432c03ca8e550b373
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/utilities/modcrop.m
@@ -0,0 +1,12 @@
+function imgs = modcrop(imgs, modulo)
+if size(imgs,3)==1
+    sz = size(imgs);
+    sz = sz - mod(sz, modulo);
+    imgs = imgs(1:sz(1), 1:sz(2));
+else
+    tmpsz = size(imgs);
+    sz = tmpsz(1:2);
+    sz = sz - mod(sz, modulo);
+    imgs = imgs(1:sz(1), 1:sz(2),:);
+end
+
diff --git a/datasets/Test_Datasets/FFDNet-master/utilities/shave.m b/datasets/Test_Datasets/FFDNet-master/utilities/shave.m
new file mode 100644
index 0000000000000000000000000000000000000000..9790fe39317438a2beb820cdc6c27d2b15b20d3c
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/utilities/shave.m
@@ -0,0 +1,3 @@
+function I = shave(I, border)
+I = I(1+border(1):end-border(1), ...
+      1+border(2):end-border(2), :, :);
diff --git a/datasets/Test_Datasets/FFDNet-master/utilities/vl_ffdnet_concise.m b/datasets/Test_Datasets/FFDNet-master/utilities/vl_ffdnet_concise.m
new file mode 100644
index 0000000000000000000000000000000000000000..1798e78f8865c3b6bd8f08e3448d72215914229a
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/utilities/vl_ffdnet_concise.m
@@ -0,0 +1,38 @@
+function res = vl_ffdnet_concise(net, x)
+
+global sigmas;
+n = numel(net.layers);
+res = struct('x', cell(1,n+1));
+res(1).x = x ;
+cudnn = {'CuDNN'} ;
+%cudnn = {'NoCuDNN'} ;
+
+for i=1:n
+    l = net.layers{i} ;
+    switch l.type
+        case 'conv'
+            res(i+1).x = vl_nnconv(res(i).x, l.weights{1}, l.weights{2}, ...
+                'pad', l.pad, ...
+                'stride', l.stride, ...
+                'dilate', l.dilate, ...
+                l.opts{:}, ...
+                cudnn{:}) ;
+            
+        case 'concat'
+            if size(sigmas,1)~=size(res(i).x,1)
+                sigmaMap   = bsxfun(@times,ones(size(res(i).x,1),size(res(i).x,2),1,size(res(i).x,4),'single'),permute(sigmas,[3 4 1 2]));
+                res(i+1).x = cat(3,res(i).x,sigmaMap);
+            else
+                res(i+1).x = cat(3,res(i).x,sigmaMap);
+            end
+
+        case 'SubP'
+            res(i+1).x = vl_nnSubP(res(i).x, [],'scale',l.scale);
+  
+        case 'relu'
+            res(i+1).x = max(res(i).x,0) ;
+    end
+        res(i).x = [] ;
+end
+
+
diff --git a/datasets/Test_Datasets/FFDNet-master/utilities/vl_ffdnet_matlab.m b/datasets/Test_Datasets/FFDNet-master/utilities/vl_ffdnet_matlab.m
new file mode 100644
index 0000000000000000000000000000000000000000..cd236cfd79ca076469675dcf6314d51751694f4b
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/utilities/vl_ffdnet_matlab.m
@@ -0,0 +1,42 @@
+function res = vl_ffdnet_matlab(net, input)
+
+%% If you did not install the matconvnet package, you can use this for testing.
+
+global sigmas;
+n = numel(net.layers);
+res = struct('x', cell(1,n+1));
+res(1).x = input;
+
+for i = 1 : n
+    l = net.layers{i};
+    switch l.type
+        
+        case 'conv'
+            disp(['Processing ... ',int2str(i),'/',int2str(n)]);
+            for noutmaps = 1 : size(l.weights{1},4)
+                z = zeros(size(res(i).x,1),size(res(i).x,2),'single');
+                for ninmaps = 1 : size(res(i).x,3)
+                    z = z + convn(res(i).x(:,:,ninmaps), rot90(l.weights{1}(:,:,ninmaps,noutmaps),2),'same'); % 180 degree rotation for kernel
+                end
+                res(i+1).x(:,:,noutmaps) = z + l.weights{2}(noutmaps);
+            end
+            
+        case 'relu'
+            res(i+1).x = max(res(i).x,0);
+            
+        case 'concat'
+            if size(sigmas,1)~=size(res(i).x,1)
+                sigmaMap   = bsxfun(@times,ones(size(res(i).x,1),size(res(i).x,2),1,size(res(i).x,4),'single'),permute(sigmas,[3 4 1 2]));
+                res(i+1).x = cat(3,res(i).x,sigmaMap);
+            else
+                res(i+1).x = cat(3,res(i).x,sigmaMap);
+            end
+            
+        case 'SubP'
+            res(i+1).x = vl_nnSubP(res(i).x, [],'scale',l.scale);
+            
+    end
+    res(i).x = [];
+end
+
+end
diff --git a/datasets/Test_Datasets/FFDNet-master/utilities/vl_nnSubP.m b/datasets/Test_Datasets/FFDNet-master/utilities/vl_nnSubP.m
new file mode 100644
index 0000000000000000000000000000000000000000..acdc9bce80ce7b03d43fe483ad8ed7958cdb4341
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/utilities/vl_nnSubP.m
@@ -0,0 +1,78 @@
+function y = vl_nnSubP(x, dzdy, varargin)
+
+
+opts.scale = 2;
+opts = vl_argparse(opts, varargin, 'nonrecursive') ;
+scale = opts.scale;
+
+if scale > 1 % many (small)---> one (big) e.g., 100X100X256X128  ----> 200X200X64*128
+    scale2 = scale^2;
+    if nargin <= 1 || isempty(dzdy)
+        [hei,wid,channelI,bsize] = size(x);
+        channelO = channelI/scale2;
+        channelI_start = 0;
+        idx1 = scale * (1:hei);
+        idx2 = scale * (1:wid);
+        y = zeros([scale*hei, scale*wid, channelO, bsize],'like',x);
+        for nchannelO = 1:channelO
+            for i = 1:scale2
+                a = mod(i-1, scale) + 1;
+                b = floor((i-1)/scale) + 1;
+                y(idx1+a-scale, idx2+b-scale, nchannelO, :) = x(:,:,channelI_start+i,:);
+            end
+            channelI_start = scale2 + channelI_start;
+        end
+    else
+        [hei,wid,channelO,bsize] = size(dzdy);
+        idx1 = 1:scale:hei;
+        idx2 = 1:scale:wid;
+        channelI = channelO*scale2;
+        channelI_start = 0;
+        y = zeros([hei/scale, wid/scale, channelI, bsize],'like',x);
+        for  nchannelO = 1:channelO
+            for i = 1:scale^2
+                a = mod(i-1, scale) + 1;
+                b = floor((i-1)/scale) + 1;
+                y(:, :, channelI_start+i, :) = dzdy(idx1+a-1, idx2+b-1,nchannelO,:);
+            end
+            channelI_start = scale2 + channelI_start;
+        end
+    end
+    
+else %  one (big) ---> many (small)  e.g., 200X200X64*128--->100X100X256X128
+    
+    scale = round(1/scale);
+    scale2 = scale^2;
+    if nargin <= 1 || isempty(dzdy)
+        [hei,wid,channelO,bsize] = size(x);
+        idx1 = 1:scale:hei;
+        idx2 = 1:scale:wid;
+        channelI = channelO*scale2;
+        channelI_start = 0;
+        y = zeros([hei/scale, wid/scale, channelI, bsize],'like',x);
+        for  nchannelO = 1:channelO
+            for i = 1:scale^2
+                a = mod(i-1, scale) + 1;
+                b = floor((i-1)/scale) + 1;
+                y(:, :, channelI_start+i, :) = x(idx1+a-1, idx2+b-1,nchannelO,:);
+            end
+            channelI_start = scale2 + channelI_start;
+        end
+    else
+        [hei,wid,channelI,bsize] = size(dzdy);
+        channelO = channelI/scale2;
+        channelI_start = 0;
+        idx1 = scale * (1:hei);
+        idx2 = scale * (1:wid);
+        y = zeros([scale*hei, scale*wid, channelO, bsize],'like',x);
+        for nchannelO = 1:channelO
+            for i = 1:scale2
+                a = mod(i-1, scale) + 1;
+                b = floor((i-1)/scale) + 1;
+                y(idx1+a-scale, idx2+b-scale, nchannelO, :) = dzdy(:,:,channelI_start+i,:);
+            end
+            channelI_start = scale2 + channelI_start;
+        end
+    end
+end
+
diff --git a/datasets/Test_Datasets/FFDNet-master/utilities/vl_randnm.m b/datasets/Test_Datasets/FFDNet-master/utilities/vl_randnm.m
new file mode 100644
index 0000000000000000000000000000000000000000..593a7baaf858ad68c0fe712be99865e5a686d9d8
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/utilities/vl_randnm.m
@@ -0,0 +1,14 @@
+function [noise,sigma6] = vl_randnm(w, h, Sigma)
+
+% generate noise
+mu     =  [0,0,0];
+noise  =  mvnrnd(mu,Sigma,w*h);
+noise  =  reshape(noise,[w,h,3]);
+
+% extract 6 parameters
+sigma  =  sqrt(Sigma);
+sigma  =  sigma(:);
+sigma6 =  sigma([1,4,5,7,8,9]);
+
+end
+
diff --git a/datasets/Test_Datasets/FFDNet-master/utilities/vl_simplenn.m b/datasets/Test_Datasets/FFDNet-master/utilities/vl_simplenn.m
new file mode 100644
index 0000000000000000000000000000000000000000..4818fff836af7a4ec6858e7e091a271f1d9fc87d
--- /dev/null
+++ b/datasets/Test_Datasets/FFDNet-master/utilities/vl_simplenn.m
@@ -0,0 +1,137 @@
+function res = vl_simplenn(net, x, dzdy, res, varargin)
+%VL_SIMPLENN  Evaluate a SimpleNN network.
+%   RES = VL_SIMPLENN(NET, X) evaluates the convnet NET on data X.
+%   RES = VL_SIMPLENN(NET, X, DZDY) evaluates the convnent NET and its
+%   derivative on data X and output derivative DZDY (foward+bacwkard pass).
+%   RES = VL_SIMPLENN(NET, X, [], RES) evaluates the NET on X reusing the
+%   structure RES.
+%   RES = VL_SIMPLENN(NET, X, DZDY, RES) evaluates the NET on X and its
+%   derivatives reusing the structure RES.
+%
+%   This function process networks using the SimpleNN wrapper
+%   format. Such networks are 'simple' in the sense that they consist
+%   of a linear sequence of computational layers. You can use the
+%   `dagnn.DagNN` wrapper for more complex topologies, or write your
+%   own wrapper around MatConvNet computational blocks for even
+%   greater flexibility.
+
+% Copyright (C) 2014-15 Andrea Vedaldi.
+% All rights reserved.
+%
+% This file is part of the VLFeat library and is made available under
+% the terms of the BSD license (see the COPYING file).
+global sigmas;
+opts.conserveMemory = false ;
+opts.sync = false ;
+opts.mode = 'normal' ;
+opts.accumulate = false ;
+opts.cudnn = true ;
+opts.backPropDepth = +inf ;
+opts.skipForward = false ;
+opts.parameterServer = [] ;
+opts.holdOn = false ;
+opts = vl_argparse(opts, varargin);
+
+n = numel(net.layers) ;
+assert(opts.backPropDepth > 0, 'Invalid `backPropDepth` value (!>0)');
+backPropLim = max(n - opts.backPropDepth + 1, 1);
+
+if (nargin <= 2) || isempty(dzdy)
+    doder = false ;
+    if opts.skipForward
+        error('simplenn:skipForwardNoBackwPass', ...
+            '`skipForward` valid only when backward pass is computed.');
+    end
+else
+    doder = true ;
+end
+
+if opts.cudnn
+    cudnn = {'CuDNN'} ;
+    bnormCudnn = {'NoCuDNN'} ; % ours seems slighty faster
+else
+    cudnn = {'NoCuDNN'} ;
+    bnormCudnn = {'NoCuDNN'} ;
+end
+
+switch lower(opts.mode)
+    case 'normal'
+        testMode = false ;
+    case 'test'
+        testMode = true ;
+    otherwise
+        error('Unknown mode ''%s''.', opts. mode) ;
+end
+
+gpuMode = isa(x, 'gpuArray') ;
+
+if nargin <= 3 || isempty(res)
+    if opts.skipForward
+        error('simplenn:skipForwardEmptyRes', ...
+            'RES structure must be provided for `skipForward`.');
+    end
+    res = struct(...
+        'x', cell(1,n+1), ...
+        'dzdx', cell(1,n+1), ...
+        'dzdw', cell(1,n+1), ...
+        'aux', cell(1,n+1), ...
+        'stats', cell(1,n+1), ...
+        'time', num2cell(zeros(1,n+1)), ...
+        'backwardTime', num2cell(zeros(1,n+1))) ;
+end
+
+if ~opts.skipForward
+    res(1).x = x ;
+end
+
+% -------------------------------------------------------------------------
+%                                                              Forward pass
+% -------------------------------------------------------------------------
+
+for i=1:n
+    if opts.skipForward, break; end;
+    l = net.layers{i} ;
+    %res(i).time = tic ;
+    switch l.type
+        case 'conv'
+            res(i+1).x = vl_nnconv(res(i).x, l.weights{1}, l.weights{2}, ...
+                'pad', l.pad, ...
+                'stride', l.stride, ...
+                'dilate', l.dilate, ...
+                l.opts{:}, ...
+                cudnn{:}) ;
+            
+        case 'concat'
+            if size(sigmas,1)~=size(res(i).x,1)
+                sigmaMap   = bsxfun(@times,ones(size(res(i).x,1),size(res(i).x,2),1,size(res(i).x,4)),permute(sigmas,[3 4 1 2])) ;
+                res(i+1).x = vl_nnconcat({res(i).x,sigmaMap}) ;
+            else
+                res(i+1).x = vl_nnconcat({res(i).x,sigmas}) ;
+            end
+            
+        case 'SubP'
+            res(i+1).x = vl_nnSubP(res(i).x, [],'scale',l.scale) ;
+        case 'relu'
+            leak = {} ; 
+            res(i+1).x = vl_nnrelu(res(i).x,[],leak{:}) ;
+    end
+    
+    % optionally forget intermediate results
+    needsBProp = doder && i >= backPropLim;
+    forget = opts.conserveMemory && ~needsBProp ;
+    if i > 1
+        lp = net.layers{i-1} ;
+        % forget RELU input, even for BPROP
+        forget = forget && (~needsBProp || (strcmp(l.type, 'relu') && ~lp.precious)) ;
+        forget = forget && ~(strcmp(lp.type, 'loss') || strcmp(lp.type, 'softmaxloss')) ;
+        forget = forget && ~lp.precious ;
+    end
+    if forget
+        res(i).x = [] ;
+    end
+    
+    if gpuMode && opts.sync
+        wait(gpuDevice) ;
+    end
+    %res(i).time = toc(res(i).time) ;
+end
diff --git a/download_data.py b/download_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5b460f102e5676de307f0b9b6e0f23d94fae36b
--- /dev/null
+++ b/download_data.py
@@ -0,0 +1,95 @@
+import os
+import requests
+import zipfile
+import tarfile
+from tqdm import tqdm
+
+# Target directory for all datasets
+BASE_DIR = "./datasets"
+
+# URLs for the industry-standard high-res and benchmark datasets
+DATASETS = {
+    # High-Resolution Training & Validation (for H100s)
+    
+    #"Flickr2K": "https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar",
+    
+    # Standard Benchmark Test Sets (Hosted reliably on popular CV repos)
+    "Test_Datasets": "https://github.com/cszn/FFDNet/archive/refs/heads/master.zip" 
+}
+
+
+def download_file(url, dest_path):
+    """Downloads a file with a progress bar and robust error handling."""
+    if os.path.exists(dest_path):
+        print(f"[*] {os.path.basename(dest_path)} already exists. Skipping download.")
+        return
+
+    print(f"[*] Downloading {url}...")
+    
+    # Disguise the script as a standard web browser
+    headers = {
+        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
+    }
+    
+    try:
+        response = requests.get(url, stream=True, headers=headers, timeout=30)
+        response.raise_for_status()
+        
+        total_size = int(response.headers.get('content-length', 0))
+        block_size = 1024 * 1024 # 1 MB chunks
+        
+        with open(dest_path, 'wb') as file, tqdm(
+            total=total_size, unit='B', unit_scale=True, desc=os.path.basename(dest_path)
+        ) as bar:
+            for data in response.iter_content(block_size):
+                file.write(data)
+                bar.update(len(data))
+                
+    except requests.exceptions.RequestException as e:
+        print(f"\n[!] The server rejected the connection: {e}")
+        print(f"[!] Skipping {os.path.basename(dest_path)}. You can proceed without it.")
+        # Remove the partial file if it failed midway
+        if os.path.exists(dest_path):
+            os.remove(dest_path)
+
+def extract_file(file_path, extract_to):
+    """Extracts zip or tar files."""
+    print(f"[*] Extracting {os.path.basename(file_path)}...")
+    
+    if file_path.endswith(".zip"):
+        with zipfile.ZipFile(file_path, 'r') as zip_ref:
+            zip_ref.extractall(extract_to)
+    elif file_path.endswith(".tar") or file_path.endswith(".tar.gz"):
+        with tarfile.open(file_path, 'r:*') as tar_ref:
+            tar_ref.extractall(extract_to)
+    else:
+        print(f"[!] Unknown file format for {file_path}")
+
+def main():
+    os.makedirs(BASE_DIR, exist_ok=True)
+    
+    for name, url in DATASETS.items():
+        print(f"\n--- Processing {name} ---")
+        
+        # Determine file extension and destination paths
+        ext = ".tar" if ".tar" in url else ".zip"
+        file_name = f"{name}{ext}"
+        download_path = os.path.join(BASE_DIR, file_name)
+        
+        # Download
+        download_file(url, download_path)
+        
+        # Extract
+        extract_dir = os.path.join(BASE_DIR, name)
+        os.makedirs(extract_dir, exist_ok=True)
+        extract_file(download_path, extract_dir)
+        
+        # Clean up the archive to save disk space
+        print(f"[*] Cleaning up archive {file_name}...")
+        os.remove(download_path)
+
+    print("\n[+] All datasets downloaded and extracted successfully!")
+    print(f"[+] Look inside the '{BASE_DIR}' folder.")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/evaluate_checkpoints.py b/evaluate_checkpoints.py
new file mode 100644
index 0000000000000000000000000000000000000000..a65e3fbf0b978297bfb2f7a9fa24b1252b5f0d88
--- /dev/null
+++ b/evaluate_checkpoints.py
@@ -0,0 +1,259 @@
+import argparse
+import os
+
+import torch
+from PIL import Image
+from torchvision import transforms
+
+from train_network import (
+    DEFAULT_GAMMAS,
+    DEFAULT_SIGMA_LEVELS,
+    DEFAULT_TAU_INITS,
+    _collect_image_paths,
+    _SCRIPT_DIR,
+    calculate_psnr,
+    gamma_tag,
+    sigma_int_to_float,
+    sigma_tag,
+    tau_tag,
+)
+from train_network import UnrolledNetwork as MLPUnrolledNetwork
+from train_network_rbf import UnrolledNetwork as RBFUnrolledNetwork
+from train_tnrd_baseline import TNRDBaselineNetwork
+
+
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+TESTSETS = ("Set12", "BSD68")
+
+
+def _testset_root(name):
+    return os.path.join(
+        _SCRIPT_DIR,
+        "datasets",
+        "Test_Datasets",
+        "FFDNet-master",
+        "testsets",
+        name,
+    )
+
+
+def _autocast_context():
+    return (
+        torch.amp.autocast("cuda")
+        if DEVICE.type == "cuda"
+        else torch.autocast("cpu", enabled=False)
+    )
+
+
+def _build_model(model_type, stages, use_wave, damping_gamma, tau_init):
+    if model_type == "mlp":
+        return MLPUnrolledNetwork(stages, use_wave, damping_gamma=damping_gamma, tau_init=tau_init).to(DEVICE)
+    if model_type == "rbf":
+        return RBFUnrolledNetwork(stages, use_wave, damping_gamma=damping_gamma, tau_init=tau_init).to(DEVICE)
+    if model_type == "tnrd":
+        return TNRDBaselineNetwork(stages, tau_init=tau_init).to(DEVICE)
+    raise ValueError(f"Unknown model type: {model_type}")
+
+
+def _checkpoint_specs(stages, sigmas, gammas, tau_inits, include_finetuned):
+    specs = []
+
+    for sigma in sigmas:
+        sigma_name = sigma_tag(sigma)
+        for tau_init in tau_inits:
+            tau_name = tau_tag(tau_init)
+            specs.append(
+                {
+                    "label": f"TNRD baseline sigma={sigma} tau={tau_init}",
+                    "model_type": "tnrd",
+                    "use_wave": False,
+                    "damping_gamma": 1.0,
+                    "tau_init": tau_init,
+                    "path": f"tnrd_baseline_{stages}stages_{sigma_name}_{tau_name}.pth",
+                }
+            )
+            if include_finetuned:
+                specs.append(
+                    {
+                        "label": f"Finetuned TNRD baseline sigma={sigma} tau={tau_init}",
+                        "model_type": "tnrd",
+                        "use_wave": False,
+                        "damping_gamma": 1.0,
+                        "tau_init": tau_init,
+                        "path": f"finetuned_tnrd_baseline_{stages}stages_{sigma_name}_{tau_name}.pth",
+                    }
+                )
+
+        for damping_gamma in gammas:
+            gamma_name = gamma_tag(damping_gamma)
+            for tau_init in tau_inits:
+                tau_name = tau_tag(tau_init)
+                specs.extend(
+                    [
+                        {
+                            "label": f"MLP Telegraph sigma={sigma} gamma={damping_gamma} tau={tau_init}",
+                            "model_type": "mlp",
+                            "use_wave": True,
+                            "damping_gamma": damping_gamma,
+                            "tau_init": tau_init,
+                            "path": f"model_{stages}stages_waveTrue_{sigma_name}_{gamma_name}_{tau_name}.pth",
+                        },
+                        {
+                            "label": f"MLP No-wave sigma={sigma} gamma={damping_gamma} tau={tau_init}",
+                            "model_type": "mlp",
+                            "use_wave": False,
+                            "damping_gamma": damping_gamma,
+                            "tau_init": tau_init,
+                            "path": f"model_{stages}stages_waveFalse_{sigma_name}_{gamma_name}_{tau_name}.pth",
+                        },
+                        {
+                            "label": f"RBF Telegraph sigma={sigma} gamma={damping_gamma} tau={tau_init}",
+                            "model_type": "rbf",
+                            "use_wave": True,
+                            "damping_gamma": damping_gamma,
+                            "tau_init": tau_init,
+                            "path": f"rbf_model_{stages}stages_waveTrue_{sigma_name}_{gamma_name}_{tau_name}.pth",
+                        },
+                        {
+                            "label": f"RBF No-wave sigma={sigma} gamma={damping_gamma} tau={tau_init}",
+                            "model_type": "rbf",
+                            "use_wave": False,
+                            "damping_gamma": damping_gamma,
+                            "tau_init": tau_init,
+                            "path": f"rbf_model_{stages}stages_waveFalse_{sigma_name}_{gamma_name}_{tau_name}.pth",
+                        },
+                    ]
+                )
+
+                if include_finetuned:
+                    specs.extend(
+                        [
+                            {
+                                "label": f"Finetuned MLP Telegraph sigma={sigma} gamma={damping_gamma} tau={tau_init}",
+                                "model_type": "mlp",
+                                "use_wave": True,
+                                "damping_gamma": damping_gamma,
+                                "tau_init": tau_init,
+                                "path": f"finetuned_{stages}stages_waveTrue_{sigma_name}_{gamma_name}_{tau_name}.pth",
+                            },
+                            {
+                                "label": f"Finetuned MLP No-wave sigma={sigma} gamma={damping_gamma} tau={tau_init}",
+                                "model_type": "mlp",
+                                "use_wave": False,
+                                "damping_gamma": damping_gamma,
+                                "tau_init": tau_init,
+                                "path": f"finetuned_{stages}stages_waveFalse_{sigma_name}_{gamma_name}_{tau_name}.pth",
+                            },
+                            {
+                                "label": f"Finetuned RBF Telegraph sigma={sigma} gamma={damping_gamma} tau={tau_init}",
+                                "model_type": "rbf",
+                                "use_wave": True,
+                                "damping_gamma": damping_gamma,
+                                "tau_init": tau_init,
+                                "path": f"finetuned_rbf_model_{stages}stages_waveTrue_{sigma_name}_{gamma_name}_{tau_name}.pth",
+                            },
+                            {
+                                "label": f"Finetuned RBF No-wave sigma={sigma} gamma={damping_gamma} tau={tau_init}",
+                                "model_type": "rbf",
+                                "use_wave": False,
+                                "damping_gamma": damping_gamma,
+                                "tau_init": tau_init,
+                                "path": f"finetuned_rbf_model_{stages}stages_waveFalse_{sigma_name}_{gamma_name}_{tau_name}.pth",
+                            },
+                        ]
+                    )
+
+    return specs
+
+
+def evaluate_checkpoint(spec, dataset_name, sigma, stages):
+    model = _build_model(
+        spec["model_type"],
+        stages,
+        spec["use_wave"],
+        spec["damping_gamma"],
+        spec["tau_init"],
+    )
+    state = torch.load(spec["path"], map_location=DEVICE)
+    model.load_state_dict(state)
+    model.eval()
+
+    test_root = _testset_root(dataset_name)
+    test_paths = _collect_image_paths(test_root)
+    if not test_paths:
+        raise FileNotFoundError(
+            f"No test images found in {os.path.abspath(test_root)} for {dataset_name}."
+        )
+
+    sigma_float = sigma_int_to_float(sigma)
+    test_transform = transforms.Compose([transforms.Grayscale(), transforms.ToTensor()])
+    torch.manual_seed(42)
+    total_psnr = 0.0
+
+    with torch.no_grad():
+        for path in test_paths:
+            clean = test_transform(Image.open(path)).unsqueeze(0).to(DEVICE)
+            noisy = torch.clamp(clean + torch.randn_like(clean) * sigma_float, 0.0, 1.0)
+            with _autocast_context():
+                output = model(noisy)
+            total_psnr += calculate_psnr(clean, output)
+
+    return total_psnr / len(test_paths)
+
+
+def main(args):
+    specs = _checkpoint_specs(
+        args.stages,
+        [int(s) for s in args.sigmas],
+        [float(g) for g in args.gammas],
+        [float(t) for t in args.tau_inits],
+        args.include_finetuned,
+    )
+    print(f"[*] Evaluating checkpoints on {', '.join(TESTSETS)}")
+    print(f"[*] Device: {DEVICE}")
+    print("-" * 90)
+    print(f"{'Model':<38} {'Dataset':<8} {'PSNR':>8}  Checkpoint")
+    print("-" * 90)
+
+    for spec in specs:
+        if not os.path.exists(spec["path"]):
+            print(f"{spec['label']:<38} {'-':<8} {'[missing]':>8}  {spec['path']}")
+            continue
+
+        sigma_value = int(spec["label"].split("sigma=")[-1])
+        for dataset_name in TESTSETS:
+            psnr = evaluate_checkpoint(spec, dataset_name, sigma_value, args.stages)
+            print(f"{spec['label']:<38} {dataset_name:<8} {psnr:>8.2f}  {spec['path']}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--stages", type=int, default=5)
+    parser.add_argument(
+        "--sigmas",
+        type=int,
+        nargs="+",
+        default=list(DEFAULT_SIGMA_LEVELS),
+        help="Noise levels to evaluate, specified in 0-255 units.",
+    )
+    parser.add_argument(
+        "--gammas",
+        type=float,
+        nargs="+",
+        default=list(DEFAULT_GAMMAS),
+        help="Fixed damping gamma values to evaluate for MLP/RBF models.",
+    )
+    parser.add_argument(
+        "--tau_inits",
+        type=float,
+        nargs="+",
+        default=list(DEFAULT_TAU_INITS),
+        help="Initial tau values to evaluate.",
+    )
+    parser.add_argument(
+        "--include_finetuned",
+        action="store_true",
+        help="Also evaluate finetuned checkpoints.",
+    )
+    args = parser.parse_args()
+    main(args)
diff --git a/evaluate_set12.py b/evaluate_set12.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce3d7ca900eaae7fc5ae8acdecd9fb74f3e8f276
--- /dev/null
+++ b/evaluate_set12.py
@@ -0,0 +1,108 @@
+import glob
+import math
+import os
+
+import torch
+from PIL import Image
+from torchvision import transforms
+
+from train_network import UnrolledNetwork
+from train_tnrd_baseline import TNRDBaselineNetwork
+
+
+SIGMA = 25.0 / 255.0
+TEST_DIR = "./datasets/Test_Datasets/FFDNet-master/testsets/Set12"
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def calculate_psnr(img1, img2):
+    mse = torch.mean((img1 - img2) ** 2)
+    if mse == 0:
+        return float("inf")
+    return 20 * math.log10(1.0 / math.sqrt(mse))
+
+
+def _make_model(spec):
+    if spec["kind"] == "telegraph":
+        return UnrolledNetwork(num_stages=spec["stages"], use_wave=spec["wave"]).to(DEVICE)
+    if spec["kind"] == "tnrd":
+        return TNRDBaselineNetwork(num_stages=spec["stages"]).to(DEVICE)
+    raise ValueError(f"Unknown model kind: {spec['kind']}")
+
+
+def _autocast_context():
+    return torch.amp.autocast("cuda") if DEVICE.type == "cuda" else torch.autocast("cpu", enabled=False)
+
+
+def evaluate_model(spec):
+    model = _make_model(spec)
+    model.load_state_dict(torch.load(spec["file"], map_location=DEVICE))
+    model.eval()
+
+    test_transform = transforms.Compose([transforms.Grayscale(), transforms.ToTensor()])
+    test_paths = sorted(glob.glob(os.path.join(TEST_DIR, "*.png")))
+    if not test_paths:
+        return "Error: No images found in Set12 directory."
+
+    torch.manual_seed(42)
+    total_psnr = 0.0
+    with torch.no_grad():
+        for path in test_paths:
+            clean = test_transform(Image.open(path)).unsqueeze(0).to(DEVICE)
+            noisy = torch.clamp(clean + torch.randn_like(clean) * SIGMA, 0.0, 1.0)
+
+            with _autocast_context():
+                output = model(noisy)
+
+            total_psnr += calculate_psnr(clean, output)
+
+    return f"{total_psnr / len(test_paths):.2f} dB"
+
+
+def main():
+    print("[*] Evaluating Checkpoints on Set12 (Sigma = 25)...")
+    print("-" * 60)
+
+    models_to_test = [
+        {
+            "name": "3-Stage TDE (Proposed)",
+            "kind": "telegraph",
+            "stages": 3,
+            "wave": True,
+            "file": "model_3stages_waveTrue.pth",
+        },
+        {
+            "name": "5-Stage TDE (Proposed)",
+            "kind": "telegraph",
+            "stages": 5,
+            "wave": True,
+            "file": "model_5stages_waveTrue.pth",
+        },
+        {
+            "name": "5-Stage Telegraph w/o wave",
+            "kind": "telegraph",
+            "stages": 5,
+            "wave": False,
+            "file": "model_5stages_waveFalse.pth",
+        },
+        {
+            "name": "5-Stage TNRD baseline",
+            "kind": "tnrd",
+            "stages": 5,
+            "file": "tnrd_baseline_5stages.pth",
+        },
+    ]
+
+    for spec in models_to_test:
+        if os.path.exists(spec["file"]):
+            score = evaluate_model(spec)
+            print(f"{spec['name']:<30} | PSNR: {score}")
+        else:
+            print(f"{spec['name']:<30} | PSNR: [File not found]")
+
+    print("-" * 60)
+    print("PDE baseline: run classical_baseline.py for the fixed telegraph/PDE comparison.")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/finetune.py b/finetune.py
new file mode 100644
index 0000000000000000000000000000000000000000..91e434a89df524d93eeada40d4298fdd345d5678
--- /dev/null
+++ b/finetune.py
@@ -0,0 +1,247 @@
+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import argparse
+from torch.utils.data import DataLoader
+from PIL import Image
+from torchvision import transforms
+
+from train_network import (
+    DEFAULT_GAMMAS,
+    DEFAULT_SIGMA_LEVELS,
+    DEFAULT_TAU_INITS,
+    UnrolledNetwork as MLPUnrolledNetwork,
+    TrainDataset,
+    calculate_psnr,
+    gamma_tag,
+    resolve_train_dir,
+    softplus_inverse,
+    _train_dir_candidates,
+    _collect_image_paths,
+    _DEFAULT_TEST,
+    sigma_int_to_float,
+    sigma_tag,
+    tau_tag,
+)
+from train_network_rbf import UnrolledNetwork as RBFUnrolledNetwork
+from train_tnrd_baseline import TNRDBaselineNetwork
+
+
+def _model_label(model_type, use_wave):
+    if model_type == "mlp":
+        return f"{'Telegraph' if use_wave else 'No-wave'} MLP model"
+    if model_type == "rbf":
+        return f"{'Telegraph' if use_wave else 'No-wave'} RBF model"
+    if model_type == "tnrd":
+        return "TNRD baseline"
+    raise ValueError(f"Unknown model type: {model_type}")
+
+
+def _build_model(model_type, stages, use_wave, damping_gamma, tau_init):
+    if model_type == "mlp":
+        return MLPUnrolledNetwork(stages, use_wave, damping_gamma=damping_gamma, tau_init=tau_init)
+    if model_type == "rbf":
+        return RBFUnrolledNetwork(stages, use_wave, damping_gamma=damping_gamma, tau_init=tau_init)
+    if model_type == "tnrd":
+        return TNRDBaselineNetwork(stages, tau_init=tau_init)
+    raise ValueError(f"Unknown model type: {model_type}")
+
+
+def _base_checkpoint_name(model_type, stages, use_wave, sigma_name, gamma_name, tau_name):
+    if model_type == "mlp":
+        return f"model_{stages}stages_wave{use_wave}_{sigma_name}_{gamma_name}_{tau_name}.pth"
+    if model_type == "rbf":
+        return f"rbf_model_{stages}stages_wave{use_wave}_{sigma_name}_{gamma_name}_{tau_name}.pth"
+    if model_type == "tnrd":
+        return f"tnrd_baseline_{stages}stages_{sigma_name}_{tau_name}.pth"
+    raise ValueError(f"Unknown model type: {model_type}")
+
+
+def _finetuned_checkpoint_name(model_type, stages, use_wave, sigma_name, gamma_name, tau_name):
+    if model_type == "mlp":
+        return f"finetuned_{stages}stages_wave{use_wave}_{sigma_name}_{gamma_name}_{tau_name}.pth"
+    if model_type == "rbf":
+        return f"finetuned_rbf_model_{stages}stages_wave{use_wave}_{sigma_name}_{gamma_name}_{tau_name}.pth"
+    if model_type == "tnrd":
+        return f"finetuned_tnrd_baseline_{stages}stages_{sigma_name}_{tau_name}.pth"
+    raise ValueError(f"Unknown model type: {model_type}")
+
+
+def fine_tune_single_config(args, sigma_value, damping_gamma, tau_init, train_root, test_root):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    sigma = sigma_int_to_float(sigma_value)
+    sigma_name = sigma_tag(sigma_value)
+    gamma_name = gamma_tag(damping_gamma)
+    tau_name = tau_tag(tau_init)
+    model_label = _model_label(args.model_type, args.use_wave)
+    print(
+        f"\n[*] Loading stage-wise weights for {args.stages}-Stage "
+        f"{model_label} at sigma={int(sigma_value)}/255, "
+        f"gamma={damping_gamma}, tau_init={tau_init}..."
+    )
+
+    test_paths = _collect_image_paths(test_root)
+    if not test_paths:
+        raise FileNotFoundError(
+            f"No test images in {os.path.abspath(test_root)}. "
+            "Run download_data.py for FFDNet testsets or pass --test_dir."
+        )
+
+    model = _build_model(
+        args.model_type,
+        args.stages,
+        args.use_wave,
+        damping_gamma=damping_gamma,
+        tau_init=tau_init,
+    ).to(device)
+
+    weight_file = _base_checkpoint_name(
+        args.model_type, args.stages, args.use_wave, sigma_name, gamma_name, tau_name
+    )
+    state = torch.load(weight_file, map_location=device)
+    model.load_state_dict(state)
+
+    for param in model.parameters():
+        param.requires_grad = True
+
+    criterion = nn.MSELoss()
+    train_loader = DataLoader(
+        TrainDataset(train_root, sigma=sigma),
+        batch_size=64,
+        shuffle=True,
+        num_workers=8,
+        pin_memory=device.type == "cuda",
+    )
+    scaler = torch.amp.GradScaler(device.type, enabled=device.type == "cuda")
+
+    optimizer = optim.Adam(model.parameters(), lr=1e-4)
+
+    print("\n--- Starting end-to-end fine-tuning ---")
+    for epoch in range(10):
+        model.train()
+        total_loss = 0
+        for clean, noisy in train_loader:
+            clean, noisy = clean.to(device), noisy.to(device)
+            optimizer.zero_grad()
+
+            with torch.amp.autocast(device.type, enabled=device.type == "cuda"):
+                output = model(noisy)
+                loss = criterion(output, clean)
+
+            scaler.scale(loss).backward()
+            scaler.step(optimizer)
+            scaler.update()
+            total_loss += loss.item()
+
+        print(
+            f"Fine-tune epoch [{epoch + 1}/10], loss: {total_loss / len(train_loader):.6f}"
+        )
+
+    print("\n[+] Fine-tuning complete! Evaluating...")
+
+    model.eval()
+    test_transform = transforms.Compose([transforms.Grayscale(), transforms.ToTensor()])
+    total_psnr = 0.0
+    with torch.no_grad():
+        for path in test_paths:
+            clean = test_transform(Image.open(path)).unsqueeze(0).to(device)
+            noisy = torch.clamp(clean + torch.randn_like(clean) * sigma, 0.0, 1.0)
+
+            with torch.amp.autocast(device.type, enabled=device.type == "cuda"):
+                output = model(noisy)
+            total_psnr += calculate_psnr(clean, output)
+
+    avg_psnr = total_psnr / len(test_paths)
+    print(
+        f"\n[!!!] Fine-tuned {model_label} PSNR ({os.path.basename(test_root)}) "
+        f"at sigma={int(sigma_value)}/255, gamma={damping_gamma}, tau_init={tau_init}: "
+        f"{avg_psnr:.2f} dB"
+    )
+    output_file = _finetuned_checkpoint_name(
+        args.model_type, args.stages, args.use_wave, sigma_name, gamma_name, tau_name
+    )
+    torch.save(model.state_dict(), output_file)
+    print(f"[+] Saved checkpoint: {output_file}")
+
+
+def fine_tune(args):
+    if args.model_type == "tnrd" and args.use_wave:
+        raise ValueError("--use_wave is only valid for MLP/RBF models, not for tnrd.")
+
+    train_root = resolve_train_dir(args.train_dir)
+    if train_root is None:
+        tried = "\n  ".join(os.path.abspath(p) for p in _train_dir_candidates())
+        raise FileNotFoundError(
+            "No training images found under datasets/. Tried:\n  "
+            f"{tried}\n"
+            "Pass --train_dir to your PNG folder (e.g. .../DIV2K_Train_HR/DIV2K_train_HR)."
+        )
+    print(f"[*] Fine-tune data: {train_root} ({len(_collect_image_paths(train_root))} images)")
+
+    test_root = args.test_dir or _DEFAULT_TEST
+    sigmas = [int(s) for s in args.sigmas]
+    gammas = [float(g) for g in args.gammas]
+    tau_inits = [float(t) for t in args.tau_inits]
+    print(f"[*] Sigma sweep: {', '.join(str(s) for s in sigmas)}")
+    if args.model_type != "tnrd":
+        print(f"[*] Gamma sweep: {', '.join(str(g) for g in gammas)}")
+    print(f"[*] Tau-init sweep: {', '.join(str(t) for t in tau_inits)}")
+    for sigma_value in sigmas:
+        if args.model_type == "tnrd":
+            for tau_init in tau_inits:
+                fine_tune_single_config(args, sigma_value, 1.0, tau_init, train_root, test_root)
+        else:
+            for damping_gamma in gammas:
+                for tau_init in tau_inits:
+                    fine_tune_single_config(
+                        args, sigma_value, damping_gamma, tau_init, train_root, test_root
+                    )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--stages", type=int, required=True)
+    parser.add_argument(
+        "--model_type",
+        type=str,
+        choices=("mlp", "rbf", "tnrd"),
+        default="mlp",
+        help="Which model family to fine-tune (default: mlp)",
+    )
+    parser.add_argument("--use_wave", action="store_true")
+    parser.add_argument(
+        "--sigmas",
+        type=int,
+        nargs="+",
+        default=list(DEFAULT_SIGMA_LEVELS),
+        help="Noise levels to sweep, specified in 0-255 units (default: 15 25 50 75)",
+    )
+    parser.add_argument(
+        "--gammas",
+        type=float,
+        nargs="+",
+        default=list(DEFAULT_GAMMAS),
+        help="Fixed damping gamma values to sweep for MLP/RBF models (default: 1.0)",
+    )
+    parser.add_argument(
+        "--tau_inits",
+        type=float,
+        nargs="+",
+        default=list(DEFAULT_TAU_INITS),
+        help="Initial tau values to sweep (default: 0.1)",
+    )
+    parser.add_argument(
+        "--train_dir",
+        type=str,
+        default=None,
+        help="Training PNG folder (default: same auto-detect as train_network.py)",
+    )
+    parser.add_argument(
+        "--test_dir",
+        type=str,
+        default=None,
+        help=f"Test image folder (default: BSD68 under FFDNet testsets)",
+    )
+    args = parser.parse_args()
+    fine_tune(args)
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+hint: Using 'master' as the name for the initial branch. This default branch name
+hint: is subject to change. To configure the initial branch name to use in all
+hint: of your new repositories, which will suppress this warning, call:
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+hint: 	git config --global init.defaultBranch <name>
+hint: 
+hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and
+hint: 'development'. The just-created branch can be renamed via this command:
+hint: 
+hint: 	git branch -m <name>
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+ create mode 100644 results.md
+ create mode 100755 run_overnight_sweep.sh
+ create mode 100755 scripts/push_to_hf.sh
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+ create mode 100644 train_network.py
+ create mode 100644 train_network_rbf.py
+ create mode 100644 train_tnrd_baseline.py
+Token is valid (permission: write).
+The token `move` has been saved to /home/llmteam/.cache/huggingface/stored_tokens
+Token has not been saved to git credential helper.
+Your token has been saved to /home/llmteam/.cache/huggingface/token
+Login successful.
+Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.
+Cannot authenticate through git-credential as no helper is defined on your machine.
+You might have to re-authenticate when pushing to the Hugging Face Hub.
+Run the following command in your terminal in case you want to set the 'store' credential helper as default.
+
+git config --global credential.helper store
+
+Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.
+Successfully created psidharth567/personal_math on the Hub.
+Your repo is now available at https://huggingface.co/psidharth567/personal_math
+[*] Pushing to https://huggingface.co/psidharth567/personal_math (this may take a long time for large checkpoints)...
+Missing or invalid credentials.
+Error: connect ECONNREFUSED /run/user/443900777/vscode-git-3d732cbeb9.sock
+    at PipeConnectWrap.afterConnect [as oncomplete] (node:net:1611:16) {
+  errno: -111,
+  code: 'ECONNREFUSED',
+  syscall: 'connect',
+  address: '/run/user/443900777/vscode-git-3d732cbeb9.sock'
+}
+Missing or invalid credentials.
+Error: connect ECONNREFUSED /run/user/443900777/vscode-git-3d732cbeb9.sock
+    at PipeConnectWrap.afterConnect [as oncomplete] (node:net:1611:16) {
+  errno: -111,
+  code: 'ECONNREFUSED',
+  syscall: 'connect',
+  address: '/run/user/443900777/vscode-git-3d732cbeb9.sock'
+}
+remote: Invalid username or password.
+fatal: Authentication failed for 'https://huggingface.co/psidharth567/personal_math/'
+--- exit: 0 ---
+Uploading LFS objects: 100% (204/204), 7.6 MB | 381 KB/s, done.
+To https://huggingface.co/psidharth567/personal_math.git
+ ! [rejected]        main -> main (fetch first)
+error: failed to push some refs to 'https://huggingface.co/psidharth567/personal_math.git'
+hint: Updates were rejected because the remote contains work that you do not
+hint: have locally. This is usually caused by another repository pushing to
+hint: the same ref. If you want to integrate the remote changes, use
+hint: 'git pull' before pushing again.
+hint: See the 'Note about fast-forwards' in 'git push --help' for details.
+DONE_EXIT:0
diff --git a/logs/hf_upload.log b/logs/hf_upload.log
new file mode 100644
index 0000000000000000000000000000000000000000..15fabeff623a0771955435c9a859c2eba51813a9
--- /dev/null
+++ b/logs/hf_upload.log
@@ -0,0 +1,8039 @@
+[*] Uploading /home/llmteam/sidharth/math to https://huggingface.co/psidharth567/personal_math (excluding .git and .env)...
+Cannot authenticate through git-credential as no helper is defined on your machine.
+You might have to re-authenticate when pushing to the Hugging Face Hub.
+Run the following command in your terminal in case you want to set the 'store' credential helper as default.
+
+git config --global credential.helper store
+
+Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.
+Successfully created psidharth567/personal_math on the Hub.
+Your repo is now available at https://huggingface.co/psidharth567/personal_math
+/home/llmteam/miniconda3/lib/python3.13/site-packages/huggingface_hub/hf_api.py:10389: UserWarning: Warnings while validating metadata in README.md:
+- empty or missing yaml metadata in repo card
+  warnings.warn(f"Warnings while validating metadata in README.md:\n{message}")
+It seems you are trying to upload a large folder at once. This might take some time and then fail if the folder is too large. For such cases, it is recommended to upload in smaller batches or to use `HfApi().upload_large_folder(...)`/`hf upload-large-folder` instead. For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/upload#upload-a-large-folder.
+Start hashing 1638 files.
+Finished hashing 1638 files.
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Processing Files (49 / 169)   :  17%|█▋        |  683MB / 4.07GB,  214MB/s  
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Processing Files (56 / 241)   :  24%|██▍       |  971MB / 4.07GB,  285MB/s  
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Processing Files (59 / 291)   :  28%|██▊       | 1.16GB / 4.07GB,  322MB/s  
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Processing Files (65 / 345)   :  34%|███▍      | 1.38GB / 4.07GB,  363MB/s  
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New Data Upload               :   1%|          |  524kB / 66.8MB,  138kB/s  
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Processing Files (77 / 377)   :  37%|███▋      | 1.52GB / 4.07GB,  379MB/s  
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Processing Files (97 / 444)   :  44%|████▍     | 1.79GB / 4.07GB,  425MB/s  
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Processing Files (109 / 503)  :  50%|████▉     | 2.02GB / 4.07GB,  460MB/s  
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New Data Upload               :   1%|          | 1.05MB /  134MB,  238kB/s  
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Processing Files (123 / 596)  :  59%|█████▉    | 2.40GB / 4.07GB,  499MB/s  
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New Data Upload               :   1%|▏         | 2.62MB /  200MB,  546kB/s  
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Processing Files (125 / 610)  :  60%|██████    | 2.46GB / 4.07GB,  492MB/s  
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New Data Upload               :   2%|▏         | 3.67MB /  200MB,  734kB/s  
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Processing Files (158 / 723)  :  72%|███████▏  | 2.93GB / 4.07GB,  563MB/s  
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New Data Upload               :   3%|▎         | 5.24MB /  200MB, 1.01MB/s  
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Processing Files (159 / 764)  :  76%|███████▌  | 3.09GB / 4.07GB,  573MB/s  
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New Data Upload               :   3%|▎         | 6.82MB /  267MB, 1.26MB/s  
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Processing Files (174 / 798)  :  80%|███████▉  | 3.24GB / 4.07GB,  579MB/s  
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Processing Files (177 / 809)  :  81%|████████  | 3.29GB / 4.07GB,  567MB/s  
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New Data Upload               :   4%|▍         | 10.5MB /  267MB, 1.81MB/s  
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Processing Files (179 / 818)  :  82%|████████▏ | 3.32GB / 4.07GB,  554MB/s  
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New Data Upload               :   5%|▌         | 14.2MB /  267MB, 2.36MB/s  
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Processing Files (183 / 832)  :  83%|████████▎ | 3.38GB / 4.07GB,  545MB/s  
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New Data Upload               :   7%|▋         | 17.8MB /  267MB, 2.88MB/s  
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Processing Files (189 / 864)  :  86%|████████▋ | 3.51GB / 4.07GB,  549MB/s  
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New Data Upload               :   8%|▊         | 22.0MB /  267MB, 3.44MB/s  
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Processing Files (203 / 897)  :  90%|█████████ | 3.67GB / 4.07GB,  556MB/s  
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New Data Upload               :  10%|▉         | 26.2MB /  267MB, 3.97MB/s  
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Processing Files (206 / 900)  :  90%|█████████ | 3.68GB / 4.07GB,  541MB/s  
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Processing Files (207 / 901)  :  91%|█████████ | 3.69GB / 4.07GB,  527MB/s  
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New Data Upload               :  11%|█         | 29.9MB /  267MB, 4.27MB/s  
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Processing Files (207 / 901)  :  91%|█████████ | 3.69GB / 4.07GB,  513MB/s  
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New Data Upload               :  13%|█▎        | 34.1MB /  267MB, 4.73MB/s  
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Processing Files (207 / 901)  :  91%|█████████ | 3.70GB / 4.07GB,  486MB/s  
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New Data Upload               :  12%|█▏        | 38.3MB /  331MB, 5.04MB/s  
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Processing Files (207 / 901)  :  91%|█████████ | 3.70GB / 4.07GB,  474MB/s  
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New Data Upload               :  12%|█▏        | 39.8MB /  331MB, 5.11MB/s  
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Processing Files (207 / 902)  :  91%|█████████ | 3.70GB / 4.07GB,  462MB/s  
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New Data Upload               :  13%|█▎        | 41.4MB /  331MB, 5.18MB/s  
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Processing Files (209 / 904)  :  91%|█████████ | 3.70GB / 4.07GB,  452MB/s  
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New Data Upload               :  14%|█▍        | 45.6MB /  331MB, 5.56MB/s  
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  ...R/DIV2K_train_HR/0004.png:  99%|█████████▉| 3.10MB / 3.13MB            
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Processing Files (209 / 904)  :  91%|█████████ | 3.71GB / 4.07GB,  432MB/s  
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New Data Upload               :  16%|█▌        | 53.5MB /  331MB, 6.22MB/s  
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Processing Files (242 / 941)  :  91%|█████████▏| 3.72GB / 4.07GB,  423MB/s  
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New Data Upload               :  17%|█▋        | 57.7MB /  331MB, 6.55MB/s  
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Processing Files (256 / 960)  :  92%|█████████▏| 3.73GB / 4.07GB,  414MB/s  
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Processing Files (275 / 985)  :  92%|█████████▏| 3.74GB / 4.07GB,  406MB/s  
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New Data Upload               :  19%|█▊        | 61.9MB /  331MB, 6.73MB/s  
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Processing Files (275 / 985)  :  92%|█████████▏| 3.74GB / 4.07GB,  398MB/s  
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Processing Files (279 / 994)  :  92%|█████████▏| 3.74GB / 4.07GB,  390MB/s  
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Processing Files (432 / 994)  :  92%|█████████▏| 3.74GB / 4.07GB,  367MB/s  
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New Data Upload               :  20%|██        | 66.8MB /  331MB, 6.55MB/s  
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Processing Files (441 / 1012) :  92%|█████████▏| 3.76GB / 4.07GB,  368MB/s  
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Processing Files (441 / 1013) :  92%|█████████▏| 3.76GB / 4.07GB,  368MB/s  
+
New Data Upload               :  21%|██        | 67.9MB /  331MB, 6.65MB/s  
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Processing Files (442 / 1014) :  92%|█████████▏| 3.76GB / 4.07GB,  369MB/s  
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  [+ 562 files]               :  89%|████████▉ | 2.13GB / 2.38GB            
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  ...R/DIV2K_train_HR/0211.png:  91%|█████████ | 5.36MB / 5.88MB            
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Processing Files (447 / 1019) :  92%|█████████▏| 3.76GB / 4.07GB,  369MB/s  
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New Data Upload               :  21%|██        | 68.9MB /  331MB, 6.76MB/s  
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  ...R/DIV2K_train_HR/0211.png:  91%|█████████ | 5.36MB / 5.88MB            
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  [+ 562 files]               :  89%|████████▉ | 2.13GB / 2.38GB            
Processing Files (447 / 1019) :  92%|█████████▏| 3.76GB / 4.07GB,  369MB/s  
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New Data Upload               :  21%|██        | 70.0MB /  331MB, 6.86MB/s  
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Processing Files (447 / 1019) :  92%|█████████▏| 3.76GB / 4.07GB,  369MB/s  
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New Data Upload               :  22%|██▏       | 72.6MB /  331MB, 7.12MB/s  
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Processing Files (447 / 1019) :  93%|█████████▎| 3.77GB / 4.07GB,  369MB/s  
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New Data Upload               :  23%|██▎       | 75.2MB /  331MB, 7.37MB/s  
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  ...R/DIV2K_train_HR/0211.png:  92%|█████████▏| 5.39MB / 5.88MB            
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Processing Files (447 / 1019) :  93%|█████████▎| 3.77GB / 4.07GB,  364MB/s  
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New Data Upload               :  23%|██▎       | 76.8MB /  331MB, 7.53MB/s  
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  ...R/DIV2K_train_HR/0211.png:  92%|█████████▏| 5.41MB / 5.88MB            
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Processing Files (447 / 1030) :  93%|█████████▎| 3.77GB / 4.07GB,  363MB/s  
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New Data Upload               :  25%|██▍       | 82.6MB /  331MB, 8.09MB/s  
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Processing Files (447 / 1030) :  93%|█████████▎| 3.78GB / 4.07GB,  359MB/s  
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New Data Upload               :  26%|██▌       | 85.7MB /  331MB, 8.40MB/s  
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Processing Files (447 / 1030) :  93%|█████████▎| 3.79GB / 4.07GB,  353MB/s  
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New Data Upload               :  28%|██▊       | 93.6MB /  331MB, 9.17MB/s  
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Processing Files (447 / 1030) :  93%|█████████▎| 3.79GB / 4.07GB,  332MB/s  
+
New Data Upload               :  31%|███       |  103MB /  331MB, 10.1MB/s  
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Processing Files (447 / 1030) :  93%|█████████▎| 3.80GB / 4.07GB,  306MB/s  
+
New Data Upload               :  34%|███▎      |  111MB /  331MB, 10.9MB/s  
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Processing Files (447 / 1030) :  94%|█████████▎| 3.81GB / 4.07GB,  279MB/s  
+
New Data Upload               :  37%|███▋      |  121MB /  331MB, 11.8MB/s  
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Processing Files (447 / 1030) :  94%|█████████▍| 3.82GB / 4.07GB,  261MB/s  
+
New Data Upload               :  39%|███▉      |  129MB /  331MB, 12.6MB/s  
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  [+ 573 files]               :  92%|█████████▏| 2.19GB / 2.39GB            
Processing Files (447 / 1030) :  94%|█████████▍| 3.83GB / 4.07GB,  240MB/s  
+
New Data Upload               :  41%|████      |  134MB /  331MB, 13.1MB/s  
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Processing Files (447 / 1030) :  94%|█████████▍| 3.83GB / 4.07GB,  227MB/s  
+
New Data Upload               :  43%|████▎     |  143MB /  331MB, 14.0MB/s  
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  [+ 573 files]               :  93%|█████████▎| 2.21GB / 2.39GB            
Processing Files (447 / 1030) :  94%|█████████▍| 3.84GB / 4.07GB,  202MB/s  
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New Data Upload               :  46%|████▌     |  153MB /  331MB, 14.9MB/s  
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  [+ 573 files]               :  93%|█████████▎| 2.22GB / 2.39GB            
Processing Files (447 / 1030) :  95%|█████████▍| 3.85GB / 4.07GB,  180MB/s  
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New Data Upload               :  49%|████▉     |  163MB /  331MB, 15.9MB/s  
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  [+ 573 files]               :  93%|█████████▎| 2.23GB / 2.39GB            
Processing Files (447 / 1030) :  95%|█████████▍| 3.87GB / 4.07GB,  154MB/s  
+
New Data Upload               :  53%|█████▎    |  174MB /  331MB, 17.0MB/s  
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  ...R/DIV2K_train_HR/0211.png:  96%|█████████▌| 5.66MB / 5.88MB            
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Processing Files (447 / 1030) :  95%|█████████▌| 3.87GB / 4.07GB,  145MB/s  
+
New Data Upload               :  55%|█████▍    |  181MB /  331MB, 17.5MB/s  
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  [+ 573 files]               :  94%|█████████▍| 2.25GB / 2.39GB            
Processing Files (447 / 1030) :  95%|█████████▌| 3.88GB / 4.07GB,  139MB/s  
+
New Data Upload               :  57%|█████▋    |  189MB /  331MB, 18.2MB/s  
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  [+ 573 files]               :  94%|█████████▍| 2.25GB / 2.39GB            
Processing Files (447 / 1030) :  95%|█████████▌| 3.88GB / 4.07GB, 94.0MB/s  
+
New Data Upload               :  58%|█████▊    |  193MB /  331MB, 18.4MB/s  
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Processing Files (447 / 1030) :  96%|█████████▌| 3.89GB / 4.07GB, 78.2MB/s  
+
New Data Upload               :  60%|██████    |  200MB /  331MB, 18.9MB/s  
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Processing Files (447 / 1030) :  96%|█████████▌| 3.90GB / 4.07GB, 64.3MB/s  
+
New Data Upload               :  62%|██████▏   |  205MB /  331MB, 19.5MB/s  
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Processing Files (447 / 1030) :  96%|█████████▌| 3.91GB / 4.07GB, 60.7MB/s  
+
New Data Upload               :  65%|██████▍   |  214MB /  331MB, 20.0MB/s  
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Processing Files (447 / 1030) :  96%|█████████▌| 3.91GB / 4.07GB, 57.8MB/s  
+
New Data Upload               :  67%|██████▋   |  223MB /  331MB, 20.4MB/s  
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  [+ 573 files]               :  96%|█████████▌| 2.28GB / 2.39GB            
Processing Files (447 / 1030) :  96%|█████████▌| 3.92GB / 4.07GB, 52.6MB/s  
+
New Data Upload               :  68%|██████▊   |  226MB /  331MB, 20.4MB/s  
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  ...R/DIV2K_train_HR/0211.png:  97%|█████████▋| 5.71MB / 5.88MB            
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  [+ 573 files]               :  96%|█████████▌| 2.29GB / 2.39GB            
Processing Files (447 / 1030) :  96%|█████████▋| 3.93GB / 4.07GB, 40.3MB/s  
+
New Data Upload               :  71%|███████   |  234MB /  331MB, 20.8MB/s  
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  ...R/DIV2K_train_HR/0211.png:  98%|█████████▊| 5.74MB / 5.88MB            
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  [+ 573 files]               :  96%|█████████▋| 2.30GB / 2.39GB            
Processing Files (447 / 1030) :  97%|█████████▋| 3.93GB / 4.07GB, 26.0MB/s  
+
New Data Upload               :  73%|███████▎  |  243MB /  331MB, 21.3MB/s  
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  ...R/DIV2K_train_HR/0211.png:  98%|█████████▊| 5.77MB / 5.88MB            
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  [+ 573 files]               :  97%|█████████▋| 2.31GB / 2.39GB            
Processing Files (447 / 1030) :  97%|█████████▋| 3.94GB / 4.07GB, 25.6MB/s  
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New Data Upload               :  76%|███████▌  |  250MB /  331MB, 22.0MB/s  
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  ...R/DIV2K_train_HR/0211.png:  98%|█████████▊| 5.78MB / 5.88MB            
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Processing Files (447 / 1030) :  97%|█████████▋| 3.95GB / 4.07GB, 25.9MB/s  
+
New Data Upload               :  79%|███████▊  |  260MB /  331MB, 22.6MB/s  
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  [+ 573 files]               :  97%|█████████▋| 2.32GB / 2.39GB            
Processing Files (447 / 1030) :  97%|█████████▋| 3.96GB / 4.07GB, 26.1MB/s  
+
New Data Upload               :  80%|████████  |  266MB /  331MB, 22.7MB/s  
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Processing Files (447 / 1030) :  97%|█████████▋| 3.96GB / 4.07GB, 26.8MB/s  
+
New Data Upload               :  83%|████████▎ |  273MB /  331MB, 23.5MB/s  
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Processing Files (447 / 1030) :  98%|█████████▊| 3.98GB / 4.07GB, 27.5MB/s  
+
New Data Upload               :  75%|███████▌  |  285MB /  380MB, 24.2MB/s  
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  [+ 828 files]               :  96%|█████████▋| 2.35GB / 2.43GB            
Processing Files (447 / 1285) :  98%|█████████▊| 3.98GB / 4.07GB, 28.1MB/s  
+
New Data Upload               :  77%|███████▋  |  293MB /  380MB, 24.8MB/s  
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  ...R/DIV2K_train_HR/0211.png:  99%|█████████▉| 5.85MB / 5.88MB            
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  ...R/DIV2K_train_HR/0214.png: 100%|█████████▉| 6.10MB / 6.13MB            
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  ...R/DIV2K_train_HR/0219.png: 100%|█████████▉| 4.27MB / 4.29MB            
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Processing Files (447 / 1285) :  98%|█████████▊| 4.00GB / 4.07GB, 29.0MB/s  
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New Data Upload               :  80%|███████▉  |  304MB /  380MB, 25.7MB/s  
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Processing Files (447 / 1285) :  98%|█████████▊| 4.00GB / 4.07GB, 29.0MB/s  
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New Data Upload               :  81%|████████  |  308MB /  380MB, 25.7MB/s  
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Processing Files (447 / 1285) :  98%|█████████▊| 4.00GB / 4.07GB, 29.1MB/s  
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New Data Upload               :  82%|████████▏ |  313MB /  380MB, 25.8MB/s  
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Processing Files (447 / 1285) :  98%|█████████▊| 4.01GB / 4.07GB, 28.9MB/s  
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New Data Upload               :  83%|████████▎ |  314MB /  380MB, 25.6MB/s  
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Processing Files (447 / 1285) :  98%|█████████▊| 4.01GB / 4.07GB, 28.1MB/s  
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New Data Upload               :  84%|████████▎ |  317MB /  380MB, 25.4MB/s  
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Processing Files (582 / 1285) :  99%|█████████▊| 4.01GB / 4.07GB, 28.3MB/s  
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New Data Upload               :  85%|████████▌ |  323MB /  380MB, 26.0MB/s  
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Processing Files (719 / 1285) :  99%|█████████▊| 4.02GB / 4.07GB, 27.6MB/s  
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New Data Upload               :  86%|████████▌ |  327MB /  380MB, 26.0MB/s  
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Processing Files (719 / 1285) :  99%|█████████▉| 4.03GB / 4.07GB, 27.8MB/s  
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New Data Upload               :  88%|████████▊ |  335MB /  380MB, 26.3MB/s  
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Processing Files (859 / 1285) :  99%|█████████▉| 4.03GB / 4.07GB, 28.0MB/s  
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New Data Upload               :  89%|████████▉ |  337MB /  380MB, 26.5MB/s  
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Processing Files (859 / 1285) :  99%|█████████▉| 4.03GB / 4.07GB, 28.4MB/s  
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New Data Upload               :  90%|████████▉ |  341MB /  380MB, 26.9MB/s  
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Processing Files (859 / 1285) :  99%|█████████▉| 4.04GB / 4.07GB, 28.8MB/s  
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New Data Upload               :  91%|█████████ |  346MB /  380MB, 27.4MB/s  
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Processing Files (859 / 1285) :  99%|█████████▉| 4.04GB / 4.07GB, 29.5MB/s  
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New Data Upload               :  93%|█████████▎|  353MB /  380MB, 28.1MB/s  
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  ...R/DIV2K_train_HR/0365.png:  98%|█████████▊| 3.27MB / 3.35MB            
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diff --git a/logs/overnight_20260408_013234/train_mlp_nowave_sigma15.log b/logs/overnight_20260408_013234/train_mlp_nowave_sigma15.log
new file mode 100644
index 0000000000000000000000000000000000000000..4134300b22deb5b3f4cb34943a0f85bc2d861493
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_mlp_nowave_sigma15.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+
+[*] Initializing 5-Stage TNRD Model for sigma=15/255...
+
+--- Training Stage 1/5 for sigma=15/255 ---
diff --git a/logs/overnight_20260408_013234/train_mlp_nowave_sigma25.log b/logs/overnight_20260408_013234/train_mlp_nowave_sigma25.log
new file mode 100644
index 0000000000000000000000000000000000000000..ad19ae53f6af1b8f23244945c62c1c9425f903bd
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_mlp_nowave_sigma25.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+
+[*] Initializing 5-Stage TNRD Model for sigma=25/255...
+
+--- Training Stage 1/5 for sigma=25/255 ---
diff --git a/logs/overnight_20260408_013234/train_mlp_nowave_sigma50.log b/logs/overnight_20260408_013234/train_mlp_nowave_sigma50.log
new file mode 100644
index 0000000000000000000000000000000000000000..210cfd44c9796c29d4a994f6dc69c97e5baa929d
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_mlp_nowave_sigma50.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+
+[*] Initializing 5-Stage TNRD Model for sigma=50/255...
+
+--- Training Stage 1/5 for sigma=50/255 ---
diff --git a/logs/overnight_20260408_013234/train_mlp_nowave_sigma75.log b/logs/overnight_20260408_013234/train_mlp_nowave_sigma75.log
new file mode 100644
index 0000000000000000000000000000000000000000..8573add2565c42acf384757fd0e45168cde3ecd2
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_mlp_nowave_sigma75.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+
+[*] Initializing 5-Stage TNRD Model for sigma=75/255...
+
+--- Training Stage 1/5 for sigma=75/255 ---
diff --git a/logs/overnight_20260408_013234/train_mlp_wave_sigma15.log b/logs/overnight_20260408_013234/train_mlp_wave_sigma15.log
new file mode 100644
index 0000000000000000000000000000000000000000..ade4292a2905c9f0f5b9705079eb36860d6354e8
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_mlp_wave_sigma15.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+
+[*] Initializing 5-Stage Telegraph Model for sigma=15/255...
+
+--- Training Stage 1/5 for sigma=15/255 ---
diff --git a/logs/overnight_20260408_013234/train_mlp_wave_sigma25.log b/logs/overnight_20260408_013234/train_mlp_wave_sigma25.log
new file mode 100644
index 0000000000000000000000000000000000000000..32cb2b847be4834306a47a9b2e4489d6ee54575e
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_mlp_wave_sigma25.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+
+[*] Initializing 5-Stage Telegraph Model for sigma=25/255...
+
+--- Training Stage 1/5 for sigma=25/255 ---
diff --git a/logs/overnight_20260408_013234/train_mlp_wave_sigma50.log b/logs/overnight_20260408_013234/train_mlp_wave_sigma50.log
new file mode 100644
index 0000000000000000000000000000000000000000..e2a8f8caac8f42c5df6860b90f0a8a1cbf3db2f5
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_mlp_wave_sigma50.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+
+[*] Initializing 5-Stage Telegraph Model for sigma=50/255...
+
+--- Training Stage 1/5 for sigma=50/255 ---
diff --git a/logs/overnight_20260408_013234/train_mlp_wave_sigma75.log b/logs/overnight_20260408_013234/train_mlp_wave_sigma75.log
new file mode 100644
index 0000000000000000000000000000000000000000..17d8ee8f10f2d3bce8e5be790bd3ce15fd5a0146
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_mlp_wave_sigma75.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+
+[*] Initializing 5-Stage Telegraph Model for sigma=75/255...
+
+--- Training Stage 1/5 for sigma=75/255 ---
diff --git a/logs/overnight_20260408_013234/train_rbf_nowave_sigma15.log b/logs/overnight_20260408_013234/train_rbf_nowave_sigma15.log
new file mode 100644
index 0000000000000000000000000000000000000000..a34b936a85d96ed2dc7c9abb498ec1f6895d0281
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_rbf_nowave_sigma15.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=15/255...
+
+--- Training stage 1/5 for sigma=15/255 ---
diff --git a/logs/overnight_20260408_013234/train_rbf_nowave_sigma25.log b/logs/overnight_20260408_013234/train_rbf_nowave_sigma25.log
new file mode 100644
index 0000000000000000000000000000000000000000..ca2f33defda8b110ffe6f4d05bd34954ce88848e
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_rbf_nowave_sigma25.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=25/255...
+
+--- Training stage 1/5 for sigma=25/255 ---
diff --git a/logs/overnight_20260408_013234/train_rbf_nowave_sigma50.log b/logs/overnight_20260408_013234/train_rbf_nowave_sigma50.log
new file mode 100644
index 0000000000000000000000000000000000000000..30f909a11f993d8fbd7fb426f0ee83026665c01e
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_rbf_nowave_sigma50.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=50/255...
+
+--- Training stage 1/5 for sigma=50/255 ---
diff --git a/logs/overnight_20260408_013234/train_rbf_nowave_sigma75.log b/logs/overnight_20260408_013234/train_rbf_nowave_sigma75.log
new file mode 100644
index 0000000000000000000000000000000000000000..66e94dd871c4dce86b7258f62e5119b395df1fe4
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_rbf_nowave_sigma75.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=75/255...
+
+--- Training stage 1/5 for sigma=75/255 ---
diff --git a/logs/overnight_20260408_013234/train_rbf_wave_sigma15.log b/logs/overnight_20260408_013234/train_rbf_wave_sigma15.log
new file mode 100644
index 0000000000000000000000000000000000000000..2e373c30c0ddcdec5377dc7a4d7f19b1d2190b92
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_rbf_wave_sigma15.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=15/255...
+
+--- Training stage 1/5 for sigma=15/255 ---
diff --git a/logs/overnight_20260408_013234/train_rbf_wave_sigma25.log b/logs/overnight_20260408_013234/train_rbf_wave_sigma25.log
new file mode 100644
index 0000000000000000000000000000000000000000..2e5010d3c8dae7dfe056fea086839eb9bff260f0
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_rbf_wave_sigma25.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=25/255...
+
+--- Training stage 1/5 for sigma=25/255 ---
diff --git a/logs/overnight_20260408_013234/train_rbf_wave_sigma50.log b/logs/overnight_20260408_013234/train_rbf_wave_sigma50.log
new file mode 100644
index 0000000000000000000000000000000000000000..608d70e2cad3634e4be62f9d7c9bc0dc7f84b1d1
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_rbf_wave_sigma50.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=50/255...
+
+--- Training stage 1/5 for sigma=50/255 ---
diff --git a/logs/overnight_20260408_013234/train_rbf_wave_sigma75.log b/logs/overnight_20260408_013234/train_rbf_wave_sigma75.log
new file mode 100644
index 0000000000000000000000000000000000000000..898e65d98674425cf5cd9a7983424438c060b880
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_rbf_wave_sigma75.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=75/255...
+
+--- Training stage 1/5 for sigma=75/255 ---
diff --git a/logs/overnight_20260408_013234/train_tnrd_sigma15.log b/logs/overnight_20260408_013234/train_tnrd_sigma15.log
new file mode 100644
index 0000000000000000000000000000000000000000..a53f429f3fb7c3b9c6f2d48b01b40d1d2a9b3bd1
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_tnrd_sigma15.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+
+[*] Initializing 5-Stage TNRD baseline for sigma=15/255...
+
+--- Training TNRD stage 1/5 for sigma=15/255 ---
diff --git a/logs/overnight_20260408_013234/train_tnrd_sigma25.log b/logs/overnight_20260408_013234/train_tnrd_sigma25.log
new file mode 100644
index 0000000000000000000000000000000000000000..83fb854497c187c7fe1b2e2a5bfb67e37e920273
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_tnrd_sigma25.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+
+[*] Initializing 5-Stage TNRD baseline for sigma=25/255...
+
+--- Training TNRD stage 1/5 for sigma=25/255 ---
diff --git a/logs/overnight_20260408_013234/train_tnrd_sigma50.log b/logs/overnight_20260408_013234/train_tnrd_sigma50.log
new file mode 100644
index 0000000000000000000000000000000000000000..a410b8bd70909ba3c545f0bf0287d04c08c3f69e
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_tnrd_sigma50.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+
+[*] Initializing 5-Stage TNRD baseline for sigma=50/255...
+
+--- Training TNRD stage 1/5 for sigma=50/255 ---
diff --git a/logs/overnight_20260408_013234/train_tnrd_sigma75.log b/logs/overnight_20260408_013234/train_tnrd_sigma75.log
new file mode 100644
index 0000000000000000000000000000000000000000..584bd95c84cfcab2b25374f3615b367ec195ed4f
--- /dev/null
+++ b/logs/overnight_20260408_013234/train_tnrd_sigma75.log
@@ -0,0 +1,6 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+
+[*] Initializing 5-Stage TNRD baseline for sigma=75/255...
+
+--- Training TNRD stage 1/5 for sigma=75/255 ---
diff --git a/logs/overnight_20260408_013608/classical_baseline.log b/logs/overnight_20260408_013608/classical_baseline.log
new file mode 100644
index 0000000000000000000000000000000000000000..05f535a6e92735e35e9dea7fbd36d3a7d7d59852
--- /dev/null
+++ b/logs/overnight_20260408_013608/classical_baseline.log
@@ -0,0 +1,9 @@
+[*] Classical Majee 2020 baseline — Set12 & BSD68
+[+] Set12  sigma=15/255  PSNR: 26.73 dB  (12 images)
+[+] Set12  sigma=50/255  PSNR: 19.55 dB  (12 images)
+[+] Set12  sigma=75/255  PSNR: 14.98 dB  (12 images)
+
+[+] BSD68  sigma=15/255  PSNR: 26.00 dB  (68 images)
+[+] BSD68  sigma=50/255  PSNR: 19.56 dB  (68 images)
+[+] BSD68  sigma=75/255  PSNR: 15.08 dB  (68 images)
+
diff --git a/logs/overnight_20260408_013608/evaluate_checkpoints.log b/logs/overnight_20260408_013608/evaluate_checkpoints.log
new file mode 100644
index 0000000000000000000000000000000000000000..7644e8b33dd5bfae7eeb737a6e60d9876f47530a
--- /dev/null
+++ b/logs/overnight_20260408_013608/evaluate_checkpoints.log
@@ -0,0 +1,85 @@
+[*] Evaluating checkpoints on Set12, BSD68
+[*] Device: cuda
+------------------------------------------------------------------------------------------
+Model                                  Dataset      PSNR  Checkpoint
+------------------------------------------------------------------------------------------
+MLP Telegraph sigma=15                 Set12       29.19  model_5stages_waveTrue_sigma15.pth
+MLP Telegraph sigma=15                 BSD68       28.31  model_5stages_waveTrue_sigma15.pth
+MLP No-wave sigma=15                   Set12       29.59  model_5stages_waveFalse_sigma15.pth
+MLP No-wave sigma=15                   BSD68       29.08  model_5stages_waveFalse_sigma15.pth
+RBF Telegraph sigma=15                 Set12       29.19  rbf_model_5stages_waveTrue_sigma15.pth
+RBF Telegraph sigma=15                 BSD68       27.97  rbf_model_5stages_waveTrue_sigma15.pth
+RBF No-wave sigma=15                   Set12       31.45  rbf_model_5stages_waveFalse_sigma15.pth
+RBF No-wave sigma=15                   BSD68       30.46  rbf_model_5stages_waveFalse_sigma15.pth
+TNRD baseline sigma=15                 Set12       31.43  tnrd_baseline_5stages_sigma15.pth
+TNRD baseline sigma=15                 BSD68       30.41  tnrd_baseline_5stages_sigma15.pth
+Finetuned MLP Telegraph sigma=15       Set12       30.63  finetuned_5stages_waveTrue_sigma15.pth
+Finetuned MLP Telegraph sigma=15       BSD68       29.90  finetuned_5stages_waveTrue_sigma15.pth
+Finetuned MLP No-wave sigma=15         Set12       30.64  finetuned_5stages_waveFalse_sigma15.pth
+Finetuned MLP No-wave sigma=15         BSD68       29.88  finetuned_5stages_waveFalse_sigma15.pth
+Finetuned RBF Telegraph sigma=15       Set12       31.51  finetuned_rbf_model_5stages_waveTrue_sigma15.pth
+Finetuned RBF Telegraph sigma=15       BSD68       30.56  finetuned_rbf_model_5stages_waveTrue_sigma15.pth
+Finetuned RBF No-wave sigma=15         Set12       31.74  finetuned_rbf_model_5stages_waveFalse_sigma15.pth
+Finetuned RBF No-wave sigma=15         BSD68       30.79  finetuned_rbf_model_5stages_waveFalse_sigma15.pth
+Finetuned TNRD baseline sigma=15       Set12       31.85  finetuned_tnrd_baseline_5stages_sigma15.pth
+Finetuned TNRD baseline sigma=15       BSD68       30.90  finetuned_tnrd_baseline_5stages_sigma15.pth
+MLP Telegraph sigma=25                 Set12       25.92  model_5stages_waveTrue_sigma25.pth
+MLP Telegraph sigma=25                 BSD68       25.06  model_5stages_waveTrue_sigma25.pth
+MLP No-wave sigma=25                   Set12       27.47  model_5stages_waveFalse_sigma25.pth
+MLP No-wave sigma=25                   BSD68       26.87  model_5stages_waveFalse_sigma25.pth
+RBF Telegraph sigma=25                 Set12       26.50  rbf_model_5stages_waveTrue_sigma25.pth
+RBF Telegraph sigma=25                 BSD68       25.52  rbf_model_5stages_waveTrue_sigma25.pth
+RBF No-wave sigma=25                   Set12       28.97  rbf_model_5stages_waveFalse_sigma25.pth
+RBF No-wave sigma=25                   BSD68       27.86  rbf_model_5stages_waveFalse_sigma25.pth
+TNRD baseline sigma=25                 Set12       28.94  tnrd_baseline_5stages_sigma25.pth
+TNRD baseline sigma=25                 BSD68       27.85  tnrd_baseline_5stages_sigma25.pth
+Finetuned MLP Telegraph sigma=25       Set12       28.03  finetuned_5stages_waveTrue_sigma25.pth
+Finetuned MLP Telegraph sigma=25       BSD68       27.30  finetuned_5stages_waveTrue_sigma25.pth
+Finetuned MLP No-wave sigma=25         Set12       28.40  finetuned_5stages_waveFalse_sigma25.pth
+Finetuned MLP No-wave sigma=25         BSD68       27.61  finetuned_5stages_waveFalse_sigma25.pth
+Finetuned RBF Telegraph sigma=25       Set12       28.66  finetuned_rbf_model_5stages_waveTrue_sigma25.pth
+Finetuned RBF Telegraph sigma=25       BSD68       27.70  finetuned_rbf_model_5stages_waveTrue_sigma25.pth
+Finetuned RBF No-wave sigma=25         Set12       29.23  finetuned_rbf_model_5stages_waveFalse_sigma25.pth
+Finetuned RBF No-wave sigma=25         BSD68       28.30  finetuned_rbf_model_5stages_waveFalse_sigma25.pth
+Finetuned TNRD baseline sigma=25       Set12       29.33  finetuned_tnrd_baseline_5stages_sigma25.pth
+Finetuned TNRD baseline sigma=25       BSD68       28.36  finetuned_tnrd_baseline_5stages_sigma25.pth
+MLP Telegraph sigma=50                 Set12       23.32  model_5stages_waveTrue_sigma50.pth
+MLP Telegraph sigma=50                 BSD68       22.86  model_5stages_waveTrue_sigma50.pth
+MLP No-wave sigma=50                   Set12       24.54  model_5stages_waveFalse_sigma50.pth
+MLP No-wave sigma=50                   BSD68       23.87  model_5stages_waveFalse_sigma50.pth
+RBF Telegraph sigma=50                 Set12       23.32  rbf_model_5stages_waveTrue_sigma50.pth
+RBF Telegraph sigma=50                 BSD68       22.70  rbf_model_5stages_waveTrue_sigma50.pth
+RBF No-wave sigma=50                   Set12       25.43  rbf_model_5stages_waveFalse_sigma50.pth
+RBF No-wave sigma=50                   BSD68       24.47  rbf_model_5stages_waveFalse_sigma50.pth
+TNRD baseline sigma=50                 Set12       25.54  tnrd_baseline_5stages_sigma50.pth
+TNRD baseline sigma=50                 BSD68       24.58  tnrd_baseline_5stages_sigma50.pth
+Finetuned MLP Telegraph sigma=50       Set12       24.76  finetuned_5stages_waveTrue_sigma50.pth
+Finetuned MLP Telegraph sigma=50       BSD68       24.42  finetuned_5stages_waveTrue_sigma50.pth
+Finetuned MLP No-wave sigma=50         Set12       25.10  finetuned_5stages_waveFalse_sigma50.pth
+Finetuned MLP No-wave sigma=50         BSD68       24.60  finetuned_5stages_waveFalse_sigma50.pth
+Finetuned RBF Telegraph sigma=50       Set12       25.23  finetuned_rbf_model_5stages_waveTrue_sigma50.pth
+Finetuned RBF Telegraph sigma=50       BSD68       24.65  finetuned_rbf_model_5stages_waveTrue_sigma50.pth
+Finetuned RBF No-wave sigma=50         Set12       25.92  finetuned_rbf_model_5stages_waveFalse_sigma50.pth
+Finetuned RBF No-wave sigma=50         BSD68       25.23  finetuned_rbf_model_5stages_waveFalse_sigma50.pth
+Finetuned TNRD baseline sigma=50       Set12       26.05  finetuned_tnrd_baseline_5stages_sigma50.pth
+Finetuned TNRD baseline sigma=50       BSD68       25.43  finetuned_tnrd_baseline_5stages_sigma50.pth
+MLP Telegraph sigma=75                 Set12       20.99  model_5stages_waveTrue_sigma75.pth
+MLP Telegraph sigma=75                 BSD68       20.88  model_5stages_waveTrue_sigma75.pth
+MLP No-wave sigma=75                   Set12       22.28  model_5stages_waveFalse_sigma75.pth
+MLP No-wave sigma=75                   BSD68       21.71  model_5stages_waveFalse_sigma75.pth
+RBF Telegraph sigma=75                 Set12       21.36  rbf_model_5stages_waveTrue_sigma75.pth
+RBF Telegraph sigma=75                 BSD68       20.89  rbf_model_5stages_waveTrue_sigma75.pth
+RBF No-wave sigma=75                   Set12       23.12  rbf_model_5stages_waveFalse_sigma75.pth
+RBF No-wave sigma=75                   BSD68       22.30  rbf_model_5stages_waveFalse_sigma75.pth
+TNRD baseline sigma=75                 Set12       23.20  tnrd_baseline_5stages_sigma75.pth
+TNRD baseline sigma=75                 BSD68       22.42  tnrd_baseline_5stages_sigma75.pth
+Finetuned MLP Telegraph sigma=75       Set12       22.38  finetuned_5stages_waveTrue_sigma75.pth
+Finetuned MLP Telegraph sigma=75       BSD68       22.40  finetuned_5stages_waveTrue_sigma75.pth
+Finetuned MLP No-wave sigma=75         Set12       23.12  finetuned_5stages_waveFalse_sigma75.pth
+Finetuned MLP No-wave sigma=75         BSD68       22.93  finetuned_5stages_waveFalse_sigma75.pth
+Finetuned RBF Telegraph sigma=75       Set12       23.51  finetuned_rbf_model_5stages_waveTrue_sigma75.pth
+Finetuned RBF Telegraph sigma=75       BSD68       23.26  finetuned_rbf_model_5stages_waveTrue_sigma75.pth
+Finetuned RBF No-wave sigma=75         Set12       24.05  finetuned_rbf_model_5stages_waveFalse_sigma75.pth
+Finetuned RBF No-wave sigma=75         BSD68       23.74  finetuned_rbf_model_5stages_waveFalse_sigma75.pth
+Finetuned TNRD baseline sigma=75       Set12       24.18  finetuned_tnrd_baseline_5stages_sigma75.pth
+Finetuned TNRD baseline sigma=75       BSD68       23.91  finetuned_tnrd_baseline_5stages_sigma75.pth
diff --git a/logs/overnight_20260408_013608/finetune_mlp_nowave_sigma15.log b/logs/overnight_20260408_013608/finetune_mlp_nowave_sigma15.log
new file mode 100644
index 0000000000000000000000000000000000000000..60b711fcbc23f7c7e0f6f6ce7d56f1cb404ba567
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_mlp_nowave_sigma15.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+
+[*] Loading stage-wise weights for 5-Stage No-wave MLP model at sigma=15/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.001089
+Fine-tune epoch [2/10], loss: 0.001055
+Fine-tune epoch [3/10], loss: 0.001019
+Fine-tune epoch [4/10], loss: 0.000987
+Fine-tune epoch [5/10], loss: 0.000964
+Fine-tune epoch [6/10], loss: 0.000945
+Fine-tune epoch [7/10], loss: 0.000938
+Fine-tune epoch [8/10], loss: 0.000910
+Fine-tune epoch [9/10], loss: 0.000896
+Fine-tune epoch [10/10], loss: 0.000891
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned No-wave MLP model PSNR (BSD68) at sigma=15/255: 29.87 dB
+[+] Saved checkpoint: finetuned_5stages_waveFalse_sigma15.pth
diff --git a/logs/overnight_20260408_013608/finetune_mlp_nowave_sigma25.log b/logs/overnight_20260408_013608/finetune_mlp_nowave_sigma25.log
new file mode 100644
index 0000000000000000000000000000000000000000..59330b885b6b55d6cc3d7796ab295cd3c313a3c3
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_mlp_nowave_sigma25.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+
+[*] Loading stage-wise weights for 5-Stage No-wave MLP model at sigma=25/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.001814
+Fine-tune epoch [2/10], loss: 0.001697
+Fine-tune epoch [3/10], loss: 0.001645
+Fine-tune epoch [4/10], loss: 0.001622
+Fine-tune epoch [5/10], loss: 0.001598
+Fine-tune epoch [6/10], loss: 0.001540
+Fine-tune epoch [7/10], loss: 0.001539
+Fine-tune epoch [8/10], loss: 0.001525
+Fine-tune epoch [9/10], loss: 0.001531
+Fine-tune epoch [10/10], loss: 0.001464
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned No-wave MLP model PSNR (BSD68) at sigma=25/255: 27.61 dB
+[+] Saved checkpoint: finetuned_5stages_waveFalse_sigma25.pth
diff --git a/logs/overnight_20260408_013608/finetune_mlp_nowave_sigma50.log b/logs/overnight_20260408_013608/finetune_mlp_nowave_sigma50.log
new file mode 100644
index 0000000000000000000000000000000000000000..3b8a25bc2e5fee8d02b9fa9f58eee057d2163f52
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_mlp_nowave_sigma50.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+
+[*] Loading stage-wise weights for 5-Stage No-wave MLP model at sigma=50/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.003778
+Fine-tune epoch [2/10], loss: 0.003572
+Fine-tune epoch [3/10], loss: 0.003344
+Fine-tune epoch [4/10], loss: 0.003289
+Fine-tune epoch [5/10], loss: 0.003316
+Fine-tune epoch [6/10], loss: 0.003237
+Fine-tune epoch [7/10], loss: 0.003163
+Fine-tune epoch [8/10], loss: 0.003175
+Fine-tune epoch [9/10], loss: 0.003134
+Fine-tune epoch [10/10], loss: 0.003172
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned No-wave MLP model PSNR (BSD68) at sigma=50/255: 24.58 dB
+[+] Saved checkpoint: finetuned_5stages_waveFalse_sigma50.pth
diff --git a/logs/overnight_20260408_013608/finetune_mlp_nowave_sigma75.log b/logs/overnight_20260408_013608/finetune_mlp_nowave_sigma75.log
new file mode 100644
index 0000000000000000000000000000000000000000..3dfed8b9d53318fea6e5ae811fe881344e928af0
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_mlp_nowave_sigma75.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+
+[*] Loading stage-wise weights for 5-Stage No-wave MLP model at sigma=75/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.006781
+Fine-tune epoch [2/10], loss: 0.006739
+Fine-tune epoch [3/10], loss: 0.006565
+Fine-tune epoch [4/10], loss: 0.005665
+Fine-tune epoch [5/10], loss: 0.005058
+Fine-tune epoch [6/10], loss: 0.004985
+Fine-tune epoch [7/10], loss: 0.004882
+Fine-tune epoch [8/10], loss: 0.004886
+Fine-tune epoch [9/10], loss: 0.004855
+Fine-tune epoch [10/10], loss: 0.004734
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned No-wave MLP model PSNR (BSD68) at sigma=75/255: 22.92 dB
+[+] Saved checkpoint: finetuned_5stages_waveFalse_sigma75.pth
diff --git a/logs/overnight_20260408_013608/finetune_mlp_wave_sigma15.log b/logs/overnight_20260408_013608/finetune_mlp_wave_sigma15.log
new file mode 100644
index 0000000000000000000000000000000000000000..2b2f3d50f2999e56c269961b10cc119097ae8995
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_mlp_wave_sigma15.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+
+[*] Loading stage-wise weights for 5-Stage Telegraph MLP model at sigma=15/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.001099
+Fine-tune epoch [2/10], loss: 0.001004
+Fine-tune epoch [3/10], loss: 0.000961
+Fine-tune epoch [4/10], loss: 0.000934
+Fine-tune epoch [5/10], loss: 0.000910
+Fine-tune epoch [6/10], loss: 0.000906
+Fine-tune epoch [7/10], loss: 0.000876
+Fine-tune epoch [8/10], loss: 0.000875
+Fine-tune epoch [9/10], loss: 0.000871
+Fine-tune epoch [10/10], loss: 0.000862
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned Telegraph MLP model PSNR (BSD68) at sigma=15/255: 29.90 dB
+[+] Saved checkpoint: finetuned_5stages_waveTrue_sigma15.pth
diff --git a/logs/overnight_20260408_013608/finetune_mlp_wave_sigma25.log b/logs/overnight_20260408_013608/finetune_mlp_wave_sigma25.log
new file mode 100644
index 0000000000000000000000000000000000000000..9157e44d4930151d2d101122f0fdc371f2a7e679
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_mlp_wave_sigma25.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+
+[*] Loading stage-wise weights for 5-Stage Telegraph MLP model at sigma=25/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.002510
+Fine-tune epoch [2/10], loss: 0.002063
+Fine-tune epoch [3/10], loss: 0.001955
+Fine-tune epoch [4/10], loss: 0.001852
+Fine-tune epoch [5/10], loss: 0.001801
+Fine-tune epoch [6/10], loss: 0.001738
+Fine-tune epoch [7/10], loss: 0.001708
+Fine-tune epoch [8/10], loss: 0.001695
+Fine-tune epoch [9/10], loss: 0.001660
+Fine-tune epoch [10/10], loss: 0.001673
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned Telegraph MLP model PSNR (BSD68) at sigma=25/255: 27.29 dB
+[+] Saved checkpoint: finetuned_5stages_waveTrue_sigma25.pth
diff --git a/logs/overnight_20260408_013608/finetune_mlp_wave_sigma50.log b/logs/overnight_20260408_013608/finetune_mlp_wave_sigma50.log
new file mode 100644
index 0000000000000000000000000000000000000000..c3279191ef2135a75875dc81e7bb0b311daee94f
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_mlp_wave_sigma50.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+
+[*] Loading stage-wise weights for 5-Stage Telegraph MLP model at sigma=50/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.004763
+Fine-tune epoch [2/10], loss: 0.004211
+Fine-tune epoch [3/10], loss: 0.003977
+Fine-tune epoch [4/10], loss: 0.003833
+Fine-tune epoch [5/10], loss: 0.003738
+Fine-tune epoch [6/10], loss: 0.003650
+Fine-tune epoch [7/10], loss: 0.003591
+Fine-tune epoch [8/10], loss: 0.003481
+Fine-tune epoch [9/10], loss: 0.003473
+Fine-tune epoch [10/10], loss: 0.003437
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned Telegraph MLP model PSNR (BSD68) at sigma=50/255: 24.41 dB
+[+] Saved checkpoint: finetuned_5stages_waveTrue_sigma50.pth
diff --git a/logs/overnight_20260408_013608/finetune_mlp_wave_sigma75.log b/logs/overnight_20260408_013608/finetune_mlp_wave_sigma75.log
new file mode 100644
index 0000000000000000000000000000000000000000..ea8bcbeef2c4ce363c01372fd4737b427e9c77b7
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_mlp_wave_sigma75.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+
+[*] Loading stage-wise weights for 5-Stage Telegraph MLP model at sigma=75/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.008162
+Fine-tune epoch [2/10], loss: 0.006233
+Fine-tune epoch [3/10], loss: 0.005949
+Fine-tune epoch [4/10], loss: 0.005844
+Fine-tune epoch [5/10], loss: 0.005793
+Fine-tune epoch [6/10], loss: 0.005748
+Fine-tune epoch [7/10], loss: 0.005716
+Fine-tune epoch [8/10], loss: 0.005562
+Fine-tune epoch [9/10], loss: 0.005596
+Fine-tune epoch [10/10], loss: 0.005603
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned Telegraph MLP model PSNR (BSD68) at sigma=75/255: 22.39 dB
+[+] Saved checkpoint: finetuned_5stages_waveTrue_sigma75.pth
diff --git a/logs/overnight_20260408_013608/finetune_rbf_nowave_sigma15.log b/logs/overnight_20260408_013608/finetune_rbf_nowave_sigma15.log
new file mode 100644
index 0000000000000000000000000000000000000000..7384c58c6d6f09acfba00981bdf54347e76f6728
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_rbf_nowave_sigma15.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+
+[*] Loading stage-wise weights for 5-Stage No-wave RBF model at sigma=15/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.000721
+Fine-tune epoch [2/10], loss: 0.000695
+Fine-tune epoch [3/10], loss: 0.000695
+Fine-tune epoch [4/10], loss: 0.000699
+Fine-tune epoch [5/10], loss: 0.000684
+Fine-tune epoch [6/10], loss: 0.000682
+Fine-tune epoch [7/10], loss: 0.000677
+Fine-tune epoch [8/10], loss: 0.000672
+Fine-tune epoch [9/10], loss: 0.000671
+Fine-tune epoch [10/10], loss: 0.000672
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned No-wave RBF model PSNR (BSD68) at sigma=15/255: 30.79 dB
+[+] Saved checkpoint: finetuned_rbf_model_5stages_waveFalse_sigma15.pth
diff --git a/logs/overnight_20260408_013608/finetune_rbf_nowave_sigma25.log b/logs/overnight_20260408_013608/finetune_rbf_nowave_sigma25.log
new file mode 100644
index 0000000000000000000000000000000000000000..967b553db8b06e8e68581c10849ff47145d6fa24
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_rbf_nowave_sigma25.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+
+[*] Loading stage-wise weights for 5-Stage No-wave RBF model at sigma=25/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.001330
+Fine-tune epoch [2/10], loss: 0.001304
+Fine-tune epoch [3/10], loss: 0.001262
+Fine-tune epoch [4/10], loss: 0.001263
+Fine-tune epoch [5/10], loss: 0.001236
+Fine-tune epoch [6/10], loss: 0.001219
+Fine-tune epoch [7/10], loss: 0.001213
+Fine-tune epoch [8/10], loss: 0.001239
+Fine-tune epoch [9/10], loss: 0.001237
+Fine-tune epoch [10/10], loss: 0.001217
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned No-wave RBF model PSNR (BSD68) at sigma=25/255: 28.29 dB
+[+] Saved checkpoint: finetuned_rbf_model_5stages_waveFalse_sigma25.pth
diff --git a/logs/overnight_20260408_013608/finetune_rbf_nowave_sigma50.log b/logs/overnight_20260408_013608/finetune_rbf_nowave_sigma50.log
new file mode 100644
index 0000000000000000000000000000000000000000..fe67a8370be1019df0fbe933eb1285893af183c9
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_rbf_nowave_sigma50.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+
+[*] Loading stage-wise weights for 5-Stage No-wave RBF model at sigma=50/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.003188
+Fine-tune epoch [2/10], loss: 0.002938
+Fine-tune epoch [3/10], loss: 0.002783
+Fine-tune epoch [4/10], loss: 0.002775
+Fine-tune epoch [5/10], loss: 0.002710
+Fine-tune epoch [6/10], loss: 0.002667
+Fine-tune epoch [7/10], loss: 0.002654
+Fine-tune epoch [8/10], loss: 0.002657
+Fine-tune epoch [9/10], loss: 0.002626
+Fine-tune epoch [10/10], loss: 0.002642
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned No-wave RBF model PSNR (BSD68) at sigma=50/255: 25.22 dB
+[+] Saved checkpoint: finetuned_rbf_model_5stages_waveFalse_sigma50.pth
diff --git a/logs/overnight_20260408_013608/finetune_rbf_nowave_sigma75.log b/logs/overnight_20260408_013608/finetune_rbf_nowave_sigma75.log
new file mode 100644
index 0000000000000000000000000000000000000000..5480ffbc89c06cb08a7a99d3ed34303aeb9b5b13
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_rbf_nowave_sigma75.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+
+[*] Loading stage-wise weights for 5-Stage No-wave RBF model at sigma=75/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.005528
+Fine-tune epoch [2/10], loss: 0.004601
+Fine-tune epoch [3/10], loss: 0.004124
+Fine-tune epoch [4/10], loss: 0.004013
+Fine-tune epoch [5/10], loss: 0.003966
+Fine-tune epoch [6/10], loss: 0.003910
+Fine-tune epoch [7/10], loss: 0.003927
+Fine-tune epoch [8/10], loss: 0.003921
+Fine-tune epoch [9/10], loss: 0.003888
+Fine-tune epoch [10/10], loss: 0.003900
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned No-wave RBF model PSNR (BSD68) at sigma=75/255: 23.72 dB
+[+] Saved checkpoint: finetuned_rbf_model_5stages_waveFalse_sigma75.pth
diff --git a/logs/overnight_20260408_013608/finetune_rbf_wave_sigma15.log b/logs/overnight_20260408_013608/finetune_rbf_wave_sigma15.log
new file mode 100644
index 0000000000000000000000000000000000000000..48fd64ce209373717ce87a40375d04e0a51aa363
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_rbf_wave_sigma15.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+
+[*] Loading stage-wise weights for 5-Stage Telegraph RBF model at sigma=15/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.001086
+Fine-tune epoch [2/10], loss: 0.000907
+Fine-tune epoch [3/10], loss: 0.000822
+Fine-tune epoch [4/10], loss: 0.000786
+Fine-tune epoch [5/10], loss: 0.000765
+Fine-tune epoch [6/10], loss: 0.000741
+Fine-tune epoch [7/10], loss: 0.000734
+Fine-tune epoch [8/10], loss: 0.000729
+Fine-tune epoch [9/10], loss: 0.000721
+Fine-tune epoch [10/10], loss: 0.000710
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned Telegraph RBF model PSNR (BSD68) at sigma=15/255: 30.55 dB
+[+] Saved checkpoint: finetuned_rbf_model_5stages_waveTrue_sigma15.pth
diff --git a/logs/overnight_20260408_013608/finetune_rbf_wave_sigma25.log b/logs/overnight_20260408_013608/finetune_rbf_wave_sigma25.log
new file mode 100644
index 0000000000000000000000000000000000000000..6650f2a650d2bbd5ecb5192f6a5129e67fa8499b
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_rbf_wave_sigma25.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+
+[*] Loading stage-wise weights for 5-Stage Telegraph RBF model at sigma=25/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.002129
+Fine-tune epoch [2/10], loss: 0.001825
+Fine-tune epoch [3/10], loss: 0.001729
+Fine-tune epoch [4/10], loss: 0.001640
+Fine-tune epoch [5/10], loss: 0.001614
+Fine-tune epoch [6/10], loss: 0.001594
+Fine-tune epoch [7/10], loss: 0.001532
+Fine-tune epoch [8/10], loss: 0.001497
+Fine-tune epoch [9/10], loss: 0.001444
+Fine-tune epoch [10/10], loss: 0.001429
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned Telegraph RBF model PSNR (BSD68) at sigma=25/255: 27.70 dB
+[+] Saved checkpoint: finetuned_rbf_model_5stages_waveTrue_sigma25.pth
diff --git a/logs/overnight_20260408_013608/finetune_rbf_wave_sigma50.log b/logs/overnight_20260408_013608/finetune_rbf_wave_sigma50.log
new file mode 100644
index 0000000000000000000000000000000000000000..55c53b681f2cf9d3839af10a70d9c9312a5f6774
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_rbf_wave_sigma50.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+
+[*] Loading stage-wise weights for 5-Stage Telegraph RBF model at sigma=50/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.004171
+Fine-tune epoch [2/10], loss: 0.003733
+Fine-tune epoch [3/10], loss: 0.003492
+Fine-tune epoch [4/10], loss: 0.003451
+Fine-tune epoch [5/10], loss: 0.003335
+Fine-tune epoch [6/10], loss: 0.003267
+Fine-tune epoch [7/10], loss: 0.003186
+Fine-tune epoch [8/10], loss: 0.003182
+Fine-tune epoch [9/10], loss: 0.003160
+Fine-tune epoch [10/10], loss: 0.003100
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned Telegraph RBF model PSNR (BSD68) at sigma=50/255: 24.64 dB
+[+] Saved checkpoint: finetuned_rbf_model_5stages_waveTrue_sigma50.pth
diff --git a/logs/overnight_20260408_013608/finetune_rbf_wave_sigma75.log b/logs/overnight_20260408_013608/finetune_rbf_wave_sigma75.log
new file mode 100644
index 0000000000000000000000000000000000000000..93007d90e3b9cd2ceeb96bd1a6dc11f895e84156
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_rbf_wave_sigma75.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+
+[*] Loading stage-wise weights for 5-Stage Telegraph RBF model at sigma=75/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.006459
+Fine-tune epoch [2/10], loss: 0.005284
+Fine-tune epoch [3/10], loss: 0.004942
+Fine-tune epoch [4/10], loss: 0.004795
+Fine-tune epoch [5/10], loss: 0.004721
+Fine-tune epoch [6/10], loss: 0.004715
+Fine-tune epoch [7/10], loss: 0.004534
+Fine-tune epoch [8/10], loss: 0.004557
+Fine-tune epoch [9/10], loss: 0.004544
+Fine-tune epoch [10/10], loss: 0.004533
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned Telegraph RBF model PSNR (BSD68) at sigma=75/255: 23.25 dB
+[+] Saved checkpoint: finetuned_rbf_model_5stages_waveTrue_sigma75.pth
diff --git a/logs/overnight_20260408_013608/finetune_tnrd_sigma15.log b/logs/overnight_20260408_013608/finetune_tnrd_sigma15.log
new file mode 100644
index 0000000000000000000000000000000000000000..ec552bd02752962dfb53aead805c5865450444ee
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_tnrd_sigma15.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+
+[*] Loading stage-wise weights for 5-Stage TNRD baseline at sigma=15/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.000685
+Fine-tune epoch [2/10], loss: 0.000676
+Fine-tune epoch [3/10], loss: 0.000666
+Fine-tune epoch [4/10], loss: 0.000656
+Fine-tune epoch [5/10], loss: 0.000663
+Fine-tune epoch [6/10], loss: 0.000658
+Fine-tune epoch [7/10], loss: 0.000653
+Fine-tune epoch [8/10], loss: 0.000651
+Fine-tune epoch [9/10], loss: 0.000646
+Fine-tune epoch [10/10], loss: 0.000642
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned TNRD baseline PSNR (BSD68) at sigma=15/255: 30.90 dB
+[+] Saved checkpoint: finetuned_tnrd_baseline_5stages_sigma15.pth
diff --git a/logs/overnight_20260408_013608/finetune_tnrd_sigma25.log b/logs/overnight_20260408_013608/finetune_tnrd_sigma25.log
new file mode 100644
index 0000000000000000000000000000000000000000..802b773a398866f7e5ebfe803c78775f4997ad2d
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_tnrd_sigma25.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+
+[*] Loading stage-wise weights for 5-Stage TNRD baseline at sigma=25/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.001294
+Fine-tune epoch [2/10], loss: 0.001244
+Fine-tune epoch [3/10], loss: 0.001233
+Fine-tune epoch [4/10], loss: 0.001240
+Fine-tune epoch [5/10], loss: 0.001230
+Fine-tune epoch [6/10], loss: 0.001213
+Fine-tune epoch [7/10], loss: 0.001210
+Fine-tune epoch [8/10], loss: 0.001222
+Fine-tune epoch [9/10], loss: 0.001210
+Fine-tune epoch [10/10], loss: 0.001212
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned TNRD baseline PSNR (BSD68) at sigma=25/255: 28.36 dB
+[+] Saved checkpoint: finetuned_tnrd_baseline_5stages_sigma25.pth
diff --git a/logs/overnight_20260408_013608/finetune_tnrd_sigma50.log b/logs/overnight_20260408_013608/finetune_tnrd_sigma50.log
new file mode 100644
index 0000000000000000000000000000000000000000..6cbd5b4d7a56b40b2c0727e619828fc0de20275d
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_tnrd_sigma50.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+
+[*] Loading stage-wise weights for 5-Stage TNRD baseline at sigma=50/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.003016
+Fine-tune epoch [2/10], loss: 0.002788
+Fine-tune epoch [3/10], loss: 0.002722
+Fine-tune epoch [4/10], loss: 0.002631
+Fine-tune epoch [5/10], loss: 0.002587
+Fine-tune epoch [6/10], loss: 0.002574
+Fine-tune epoch [7/10], loss: 0.002584
+Fine-tune epoch [8/10], loss: 0.002549
+Fine-tune epoch [9/10], loss: 0.002533
+Fine-tune epoch [10/10], loss: 0.002485
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned TNRD baseline PSNR (BSD68) at sigma=50/255: 25.42 dB
+[+] Saved checkpoint: finetuned_tnrd_baseline_5stages_sigma50.pth
diff --git a/logs/overnight_20260408_013608/finetune_tnrd_sigma75.log b/logs/overnight_20260408_013608/finetune_tnrd_sigma75.log
new file mode 100644
index 0000000000000000000000000000000000000000..4e97962919dc874b94e05d36d63567c99010026d
--- /dev/null
+++ b/logs/overnight_20260408_013608/finetune_tnrd_sigma75.log
@@ -0,0 +1,21 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+
+[*] Loading stage-wise weights for 5-Stage TNRD baseline at sigma=75/255...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.005427
+Fine-tune epoch [2/10], loss: 0.004650
+Fine-tune epoch [3/10], loss: 0.003982
+Fine-tune epoch [4/10], loss: 0.003884
+Fine-tune epoch [5/10], loss: 0.003860
+Fine-tune epoch [6/10], loss: 0.003803
+Fine-tune epoch [7/10], loss: 0.003824
+Fine-tune epoch [8/10], loss: 0.003774
+Fine-tune epoch [9/10], loss: 0.003766
+Fine-tune epoch [10/10], loss: 0.003807
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned TNRD baseline PSNR (BSD68) at sigma=75/255: 23.89 dB
+[+] Saved checkpoint: finetuned_tnrd_baseline_5stages_sigma75.pth
diff --git a/logs/overnight_20260408_013608/train_mlp_nowave_sigma15.log b/logs/overnight_20260408_013608/train_mlp_nowave_sigma15.log
new file mode 100644
index 0000000000000000000000000000000000000000..3b30e997b00af63c678569dc641ff090d0169c65
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_mlp_nowave_sigma15.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+
+[*] Initializing 5-Stage TNRD Model for sigma=15/255...
+
+--- Training Stage 1/5 for sigma=15/255 ---
+Epoch [1/5], Loss: 0.003304
+Epoch [2/5], Loss: 0.002225
+Epoch [3/5], Loss: 0.001581
+Epoch [4/5], Loss: 0.001293
+Epoch [5/5], Loss: 0.001189
+
+--- Training Stage 2/5 for sigma=15/255 ---
+Epoch [1/5], Loss: 0.001342
+Epoch [2/5], Loss: 0.001140
+Epoch [3/5], Loss: 0.001136
+Epoch [4/5], Loss: 0.001115
+Epoch [5/5], Loss: 0.001107
+
+--- Training Stage 3/5 for sigma=15/255 ---
+Epoch [1/5], Loss: 0.001266
+Epoch [2/5], Loss: 0.001103
+Epoch [3/5], Loss: 0.001097
+Epoch [4/5], Loss: 0.001109
+Epoch [5/5], Loss: 0.001108
+
+--- Training Stage 4/5 for sigma=15/255 ---
+Epoch [1/5], Loss: 0.001367
+Epoch [2/5], Loss: 0.001111
+Epoch [3/5], Loss: 0.001098
+Epoch [4/5], Loss: 0.001106
+Epoch [5/5], Loss: 0.001095
+
+--- Training Stage 5/5 for sigma=15/255 ---
+Epoch [1/5], Loss: 0.001531
+Epoch [2/5], Loss: 0.001119
+Epoch [3/5], Loss: 0.001108
+Epoch [4/5], Loss: 0.001103
+Epoch [5/5], Loss: 0.001097
+
+[+] Training complete for sigma=15/255! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255: 29.08 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma15.pth
diff --git a/logs/overnight_20260408_013608/train_mlp_nowave_sigma25.log b/logs/overnight_20260408_013608/train_mlp_nowave_sigma25.log
new file mode 100644
index 0000000000000000000000000000000000000000..20c9dfa1e5258664268b803b2cc0820069f7a2e4
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_mlp_nowave_sigma25.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+
+[*] Initializing 5-Stage TNRD Model for sigma=25/255...
+
+--- Training Stage 1/5 for sigma=25/255 ---
+Epoch [1/5], Loss: 0.009414
+Epoch [2/5], Loss: 0.005880
+Epoch [3/5], Loss: 0.003091
+Epoch [4/5], Loss: 0.002371
+Epoch [5/5], Loss: 0.002168
+
+--- Training Stage 2/5 for sigma=25/255 ---
+Epoch [1/5], Loss: 0.002373
+Epoch [2/5], Loss: 0.002088
+Epoch [3/5], Loss: 0.002058
+Epoch [4/5], Loss: 0.002036
+Epoch [5/5], Loss: 0.001978
+
+--- Training Stage 3/5 for sigma=25/255 ---
+Epoch [1/5], Loss: 0.002050
+Epoch [2/5], Loss: 0.001919
+Epoch [3/5], Loss: 0.001915
+Epoch [4/5], Loss: 0.001881
+Epoch [5/5], Loss: 0.001846
+
+--- Training Stage 4/5 for sigma=25/255 ---
+Epoch [1/5], Loss: 0.002136
+Epoch [2/5], Loss: 0.001823
+Epoch [3/5], Loss: 0.001838
+Epoch [4/5], Loss: 0.001819
+Epoch [5/5], Loss: 0.001810
+
+--- Training Stage 5/5 for sigma=25/255 ---
+Epoch [1/5], Loss: 0.002144
+Epoch [2/5], Loss: 0.001830
+Epoch [3/5], Loss: 0.001826
+Epoch [4/5], Loss: 0.001840
+Epoch [5/5], Loss: 0.001822
+
+[+] Training complete for sigma=25/255! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255: 26.87 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma25.pth
diff --git a/logs/overnight_20260408_013608/train_mlp_nowave_sigma50.log b/logs/overnight_20260408_013608/train_mlp_nowave_sigma50.log
new file mode 100644
index 0000000000000000000000000000000000000000..efd73fff2331b8650a29229fd6bd74c0e27709cf
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_mlp_nowave_sigma50.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+
+[*] Initializing 5-Stage TNRD Model for sigma=50/255...
+
+--- Training Stage 1/5 for sigma=50/255 ---
+Epoch [1/5], Loss: 0.026357
+Epoch [2/5], Loss: 0.009844
+Epoch [3/5], Loss: 0.006499
+Epoch [4/5], Loss: 0.005476
+Epoch [5/5], Loss: 0.004931
+
+--- Training Stage 2/5 for sigma=50/255 ---
+Epoch [1/5], Loss: 0.006365
+Epoch [2/5], Loss: 0.004874
+Epoch [3/5], Loss: 0.004763
+Epoch [4/5], Loss: 0.004674
+Epoch [5/5], Loss: 0.004635
+
+--- Training Stage 3/5 for sigma=50/255 ---
+Epoch [1/5], Loss: 0.004815
+Epoch [2/5], Loss: 0.004520
+Epoch [3/5], Loss: 0.004454
+Epoch [4/5], Loss: 0.004332
+Epoch [5/5], Loss: 0.004198
+
+--- Training Stage 4/5 for sigma=50/255 ---
+Epoch [1/5], Loss: 0.004346
+Epoch [2/5], Loss: 0.004205
+Epoch [3/5], Loss: 0.004182
+Epoch [4/5], Loss: 0.004055
+Epoch [5/5], Loss: 0.003962
+
+--- Training Stage 5/5 for sigma=50/255 ---
+Epoch [1/5], Loss: 0.004192
+Epoch [2/5], Loss: 0.003855
+Epoch [3/5], Loss: 0.003860
+Epoch [4/5], Loss: 0.003900
+Epoch [5/5], Loss: 0.003937
+
+[+] Training complete for sigma=50/255! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255: 23.86 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma50.pth
diff --git a/logs/overnight_20260408_013608/train_mlp_nowave_sigma75.log b/logs/overnight_20260408_013608/train_mlp_nowave_sigma75.log
new file mode 100644
index 0000000000000000000000000000000000000000..7badcf0fa074be04345feb0baa13e282aeb0b2e5
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_mlp_nowave_sigma75.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+
+[*] Initializing 5-Stage TNRD Model for sigma=75/255...
+
+--- Training Stage 1/5 for sigma=75/255 ---
+Epoch [1/5], Loss: 0.039765
+Epoch [2/5], Loss: 0.014508
+Epoch [3/5], Loss: 0.010567
+Epoch [4/5], Loss: 0.009428
+Epoch [5/5], Loss: 0.008760
+
+--- Training Stage 2/5 for sigma=75/255 ---
+Epoch [1/5], Loss: 0.008768
+Epoch [2/5], Loss: 0.007922
+Epoch [3/5], Loss: 0.007196
+Epoch [4/5], Loss: 0.007163
+Epoch [5/5], Loss: 0.007005
+
+--- Training Stage 3/5 for sigma=75/255 ---
+Epoch [1/5], Loss: 0.007183
+Epoch [2/5], Loss: 0.006998
+Epoch [3/5], Loss: 0.006961
+Epoch [4/5], Loss: 0.006910
+Epoch [5/5], Loss: 0.006951
+
+--- Training Stage 4/5 for sigma=75/255 ---
+Epoch [1/5], Loss: 0.007194
+Epoch [2/5], Loss: 0.006938
+Epoch [3/5], Loss: 0.006998
+Epoch [4/5], Loss: 0.006995
+Epoch [5/5], Loss: 0.006931
+
+--- Training Stage 5/5 for sigma=75/255 ---
+Epoch [1/5], Loss: 0.007132
+Epoch [2/5], Loss: 0.006959
+Epoch [3/5], Loss: 0.006946
+Epoch [4/5], Loss: 0.006996
+Epoch [5/5], Loss: 0.006959
+
+[+] Training complete for sigma=75/255! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255: 21.70 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma75.pth
diff --git a/logs/overnight_20260408_013608/train_mlp_wave_sigma15.log b/logs/overnight_20260408_013608/train_mlp_wave_sigma15.log
new file mode 100644
index 0000000000000000000000000000000000000000..c71fce6c3548efb1eb30bbb9f2eb3120717badd4
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_mlp_wave_sigma15.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+
+[*] Initializing 5-Stage Telegraph Model for sigma=15/255...
+
+--- Training Stage 1/5 for sigma=15/255 ---
+Epoch [1/5], Loss: 0.002876
+Epoch [2/5], Loss: 0.001562
+Epoch [3/5], Loss: 0.001256
+Epoch [4/5], Loss: 0.001167
+Epoch [5/5], Loss: 0.001120
+
+--- Training Stage 2/5 for sigma=15/255 ---
+Epoch [1/5], Loss: 0.002616
+Epoch [2/5], Loss: 0.001969
+Epoch [3/5], Loss: 0.001606
+Epoch [4/5], Loss: 0.001455
+Epoch [5/5], Loss: 0.001374
+
+--- Training Stage 3/5 for sigma=15/255 ---
+Epoch [1/5], Loss: 0.002320
+Epoch [2/5], Loss: 0.001619
+Epoch [3/5], Loss: 0.001197
+Epoch [4/5], Loss: 0.001125
+Epoch [5/5], Loss: 0.001104
+
+--- Training Stage 4/5 for sigma=15/255 ---
+Epoch [1/5], Loss: 0.001610
+Epoch [2/5], Loss: 0.001518
+Epoch [3/5], Loss: 0.001408
+Epoch [4/5], Loss: 0.001317
+Epoch [5/5], Loss: 0.001258
+
+--- Training Stage 5/5 for sigma=15/255 ---
+Epoch [1/5], Loss: 0.001756
+Epoch [2/5], Loss: 0.001570
+Epoch [3/5], Loss: 0.001277
+Epoch [4/5], Loss: 0.001245
+Epoch [5/5], Loss: 0.001258
+
+[+] Training complete for sigma=15/255! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255: 28.30 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma15.pth
diff --git a/logs/overnight_20260408_013608/train_mlp_wave_sigma25.log b/logs/overnight_20260408_013608/train_mlp_wave_sigma25.log
new file mode 100644
index 0000000000000000000000000000000000000000..a1cb2379e45a0f7ec0b4033332e4e189372ac611
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_mlp_wave_sigma25.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+
+[*] Initializing 5-Stage Telegraph Model for sigma=25/255...
+
+--- Training Stage 1/5 for sigma=25/255 ---
+Epoch [1/5], Loss: 0.007971
+Epoch [2/5], Loss: 0.003921
+Epoch [3/5], Loss: 0.002557
+Epoch [4/5], Loss: 0.002195
+Epoch [5/5], Loss: 0.001976
+
+--- Training Stage 2/5 for sigma=25/255 ---
+Epoch [1/5], Loss: 0.006771
+Epoch [2/5], Loss: 0.005525
+Epoch [3/5], Loss: 0.004388
+Epoch [4/5], Loss: 0.003840
+Epoch [5/5], Loss: 0.003502
+
+--- Training Stage 3/5 for sigma=25/255 ---
+Epoch [1/5], Loss: 0.007468
+Epoch [2/5], Loss: 0.004506
+Epoch [3/5], Loss: 0.002596
+Epoch [4/5], Loss: 0.002403
+Epoch [5/5], Loss: 0.002348
+
+--- Training Stage 4/5 for sigma=25/255 ---
+Epoch [1/5], Loss: 0.004180
+Epoch [2/5], Loss: 0.003743
+Epoch [3/5], Loss: 0.003344
+Epoch [4/5], Loss: 0.003149
+Epoch [5/5], Loss: 0.003029
+
+--- Training Stage 5/5 for sigma=25/255 ---
+Epoch [1/5], Loss: 0.004643
+Epoch [2/5], Loss: 0.003693
+Epoch [3/5], Loss: 0.003091
+Epoch [4/5], Loss: 0.002959
+Epoch [5/5], Loss: 0.002905
+
+[+] Training complete for sigma=25/255! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255: 25.06 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma25.pth
diff --git a/logs/overnight_20260408_013608/train_mlp_wave_sigma50.log b/logs/overnight_20260408_013608/train_mlp_wave_sigma50.log
new file mode 100644
index 0000000000000000000000000000000000000000..85dbc5b0a551923700566a1b9c205a8ea2881fa9
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_mlp_wave_sigma50.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+
+[*] Initializing 5-Stage Telegraph Model for sigma=50/255...
+
+--- Training Stage 1/5 for sigma=50/255 ---
+Epoch [1/5], Loss: 0.025707
+Epoch [2/5], Loss: 0.008386
+Epoch [3/5], Loss: 0.005336
+Epoch [4/5], Loss: 0.004833
+Epoch [5/5], Loss: 0.004638
+
+--- Training Stage 2/5 for sigma=50/255 ---
+Epoch [1/5], Loss: 0.025045
+Epoch [2/5], Loss: 0.020538
+Epoch [3/5], Loss: 0.017185
+Epoch [4/5], Loss: 0.015135
+Epoch [5/5], Loss: 0.013573
+
+--- Training Stage 3/5 for sigma=50/255 ---
+Epoch [1/5], Loss: 0.032715
+Epoch [2/5], Loss: 0.016747
+Epoch [3/5], Loss: 0.005469
+Epoch [4/5], Loss: 0.004892
+Epoch [5/5], Loss: 0.004743
+
+--- Training Stage 4/5 for sigma=50/255 ---
+Epoch [1/5], Loss: 0.012154
+Epoch [2/5], Loss: 0.010279
+Epoch [3/5], Loss: 0.008407
+Epoch [4/5], Loss: 0.007630
+Epoch [5/5], Loss: 0.007203
+
+--- Training Stage 5/5 for sigma=50/255 ---
+Epoch [1/5], Loss: 0.013229
+Epoch [2/5], Loss: 0.007245
+Epoch [3/5], Loss: 0.005495
+Epoch [4/5], Loss: 0.005387
+Epoch [5/5], Loss: 0.005238
+
+[+] Training complete for sigma=50/255! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255: 22.85 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma50.pth
diff --git a/logs/overnight_20260408_013608/train_mlp_wave_sigma75.log b/logs/overnight_20260408_013608/train_mlp_wave_sigma75.log
new file mode 100644
index 0000000000000000000000000000000000000000..6c0a1101fd950e32bc381f81201e9d3e56833470
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_mlp_wave_sigma75.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+
+[*] Initializing 5-Stage Telegraph Model for sigma=75/255...
+
+--- Training Stage 1/5 for sigma=75/255 ---
+Epoch [1/5], Loss: 0.048251
+Epoch [2/5], Loss: 0.014501
+Epoch [3/5], Loss: 0.008553
+Epoch [4/5], Loss: 0.007697
+Epoch [5/5], Loss: 0.007461
+
+--- Training Stage 2/5 for sigma=75/255 ---
+Epoch [1/5], Loss: 0.047176
+Epoch [2/5], Loss: 0.038885
+Epoch [3/5], Loss: 0.032357
+Epoch [4/5], Loss: 0.028171
+Epoch [5/5], Loss: 0.025411
+
+--- Training Stage 3/5 for sigma=75/255 ---
+Epoch [1/5], Loss: 0.060110
+Epoch [2/5], Loss: 0.025439
+Epoch [3/5], Loss: 0.009463
+Epoch [4/5], Loss: 0.008402
+Epoch [5/5], Loss: 0.008188
+
+--- Training Stage 4/5 for sigma=75/255 ---
+Epoch [1/5], Loss: 0.021863
+Epoch [2/5], Loss: 0.018541
+Epoch [3/5], Loss: 0.015384
+Epoch [4/5], Loss: 0.013751
+Epoch [5/5], Loss: 0.013109
+
+--- Training Stage 5/5 for sigma=75/255 ---
+Epoch [1/5], Loss: 0.024851
+Epoch [2/5], Loss: 0.014275
+Epoch [3/5], Loss: 0.009598
+Epoch [4/5], Loss: 0.009022
+Epoch [5/5], Loss: 0.009061
+
+[+] Training complete for sigma=75/255! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255: 20.88 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma75.pth
diff --git a/logs/overnight_20260408_013608/train_rbf_nowave_sigma15.log b/logs/overnight_20260408_013608/train_rbf_nowave_sigma15.log
new file mode 100644
index 0000000000000000000000000000000000000000..0d6786ead6e36b5f3f7cb26f74681801c7ab2e41
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_rbf_nowave_sigma15.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=15/255...
+
+--- Training stage 1/5 for sigma=15/255 ---
+Epoch [1/5], loss: 0.002507
+Epoch [2/5], loss: 0.000897
+Epoch [3/5], loss: 0.000799
+Epoch [4/5], loss: 0.000769
+Epoch [5/5], loss: 0.000751
+
+--- Training stage 2/5 for sigma=15/255 ---
+Epoch [1/5], loss: 0.000886
+Epoch [2/5], loss: 0.000753
+Epoch [3/5], loss: 0.000741
+Epoch [4/5], loss: 0.000741
+Epoch [5/5], loss: 0.000742
+
+--- Training stage 3/5 for sigma=15/255 ---
+Epoch [1/5], loss: 0.000877
+Epoch [2/5], loss: 0.000744
+Epoch [3/5], loss: 0.000734
+Epoch [4/5], loss: 0.000744
+Epoch [5/5], loss: 0.000735
+
+--- Training stage 4/5 for sigma=15/255 ---
+Epoch [1/5], loss: 0.000867
+Epoch [2/5], loss: 0.000747
+Epoch [3/5], loss: 0.000726
+Epoch [4/5], loss: 0.000728
+Epoch [5/5], loss: 0.000731
+
+--- Training stage 5/5 for sigma=15/255 ---
+Epoch [1/5], loss: 0.000864
+Epoch [2/5], loss: 0.000732
+Epoch [3/5], loss: 0.000716
+Epoch [4/5], loss: 0.000725
+Epoch [5/5], loss: 0.000724
+
+[+] Training complete for sigma=15/255! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 30.46 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma15.pth
diff --git a/logs/overnight_20260408_013608/train_rbf_nowave_sigma25.log b/logs/overnight_20260408_013608/train_rbf_nowave_sigma25.log
new file mode 100644
index 0000000000000000000000000000000000000000..7d18b093efda223f9a9037751f0d91edbe5387ec
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_rbf_nowave_sigma25.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=25/255...
+
+--- Training stage 1/5 for sigma=25/255 ---
+Epoch [1/5], loss: 0.003473
+Epoch [2/5], loss: 0.001697
+Epoch [3/5], loss: 0.001546
+Epoch [4/5], loss: 0.001491
+Epoch [5/5], loss: 0.001466
+
+--- Training stage 2/5 for sigma=25/255 ---
+Epoch [1/5], loss: 0.001499
+Epoch [2/5], loss: 0.001400
+Epoch [3/5], loss: 0.001381
+Epoch [4/5], loss: 0.001375
+Epoch [5/5], loss: 0.001363
+
+--- Training stage 3/5 for sigma=25/255 ---
+Epoch [1/5], loss: 0.001473
+Epoch [2/5], loss: 0.001365
+Epoch [3/5], loss: 0.001366
+Epoch [4/5], loss: 0.001361
+Epoch [5/5], loss: 0.001375
+
+--- Training stage 4/5 for sigma=25/255 ---
+Epoch [1/5], loss: 0.001433
+Epoch [2/5], loss: 0.001362
+Epoch [3/5], loss: 0.001371
+Epoch [4/5], loss: 0.001366
+Epoch [5/5], loss: 0.001348
+
+--- Training stage 5/5 for sigma=25/255 ---
+Epoch [1/5], loss: 0.001581
+Epoch [2/5], loss: 0.001365
+Epoch [3/5], loss: 0.001365
+Epoch [4/5], loss: 0.001365
+Epoch [5/5], loss: 0.001370
+
+[+] Training complete for sigma=25/255! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 27.86 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma25.pth
diff --git a/logs/overnight_20260408_013608/train_rbf_nowave_sigma50.log b/logs/overnight_20260408_013608/train_rbf_nowave_sigma50.log
new file mode 100644
index 0000000000000000000000000000000000000000..b5799955bf004bcd0dc1ea35ad2f33b482b58525
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_rbf_nowave_sigma50.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=50/255...
+
+--- Training stage 1/5 for sigma=50/255 ---
+Epoch [1/5], loss: 0.012295
+Epoch [2/5], loss: 0.004546
+Epoch [3/5], loss: 0.004048
+Epoch [4/5], loss: 0.003878
+Epoch [5/5], loss: 0.003811
+
+--- Training stage 2/5 for sigma=50/255 ---
+Epoch [1/5], loss: 0.003575
+Epoch [2/5], loss: 0.003390
+Epoch [3/5], loss: 0.003366
+Epoch [4/5], loss: 0.003347
+Epoch [5/5], loss: 0.003378
+
+--- Training stage 3/5 for sigma=50/255 ---
+Epoch [1/5], loss: 0.003461
+Epoch [2/5], loss: 0.003302
+Epoch [3/5], loss: 0.003297
+Epoch [4/5], loss: 0.003326
+Epoch [5/5], loss: 0.003254
+
+--- Training stage 4/5 for sigma=50/255 ---
+Epoch [1/5], loss: 0.003412
+Epoch [2/5], loss: 0.003336
+Epoch [3/5], loss: 0.003298
+Epoch [4/5], loss: 0.003279
+Epoch [5/5], loss: 0.003294
+
+--- Training stage 5/5 for sigma=50/255 ---
+Epoch [1/5], loss: 0.003372
+Epoch [2/5], loss: 0.003301
+Epoch [3/5], loss: 0.003299
+Epoch [4/5], loss: 0.003319
+Epoch [5/5], loss: 0.003324
+
+[+] Training complete for sigma=50/255! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 24.46 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma50.pth
diff --git a/logs/overnight_20260408_013608/train_rbf_nowave_sigma75.log b/logs/overnight_20260408_013608/train_rbf_nowave_sigma75.log
new file mode 100644
index 0000000000000000000000000000000000000000..4c7cfedb792b9f3e15c891eef9c61f49ed84cfb5
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_rbf_nowave_sigma75.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=75/255...
+
+--- Training stage 1/5 for sigma=75/255 ---
+Epoch [1/5], loss: 0.022494
+Epoch [2/5], loss: 0.007996
+Epoch [3/5], loss: 0.007406
+Epoch [4/5], loss: 0.007200
+Epoch [5/5], loss: 0.007002
+
+--- Training stage 2/5 for sigma=75/255 ---
+Epoch [1/5], loss: 0.006648
+Epoch [2/5], loss: 0.006224
+Epoch [3/5], loss: 0.006187
+Epoch [4/5], loss: 0.006192
+Epoch [5/5], loss: 0.006074
+
+--- Training stage 3/5 for sigma=75/255 ---
+Epoch [1/5], loss: 0.006097
+Epoch [2/5], loss: 0.006103
+Epoch [3/5], loss: 0.006013
+Epoch [4/5], loss: 0.006004
+Epoch [5/5], loss: 0.006042
+
+--- Training stage 4/5 for sigma=75/255 ---
+Epoch [1/5], loss: 0.006109
+Epoch [2/5], loss: 0.006026
+Epoch [3/5], loss: 0.006050
+Epoch [4/5], loss: 0.005940
+Epoch [5/5], loss: 0.006060
+
+--- Training stage 5/5 for sigma=75/255 ---
+Epoch [1/5], loss: 0.006021
+Epoch [2/5], loss: 0.005942
+Epoch [3/5], loss: 0.005957
+Epoch [4/5], loss: 0.005924
+Epoch [5/5], loss: 0.005907
+
+[+] Training complete for sigma=75/255! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 22.30 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma75.pth
diff --git a/logs/overnight_20260408_013608/train_rbf_wave_sigma15.log b/logs/overnight_20260408_013608/train_rbf_wave_sigma15.log
new file mode 100644
index 0000000000000000000000000000000000000000..6e8e266c28b306c5f78d9c21b17c2712650d9b8f
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_rbf_wave_sigma15.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=15/255...
+
+--- Training stage 1/5 for sigma=15/255 ---
+Epoch [1/5], loss: 0.001847
+Epoch [2/5], loss: 0.000809
+Epoch [3/5], loss: 0.000756
+Epoch [4/5], loss: 0.000736
+Epoch [5/5], loss: 0.000726
+
+--- Training stage 2/5 for sigma=15/255 ---
+Epoch [1/5], loss: 0.002417
+Epoch [2/5], loss: 0.001862
+Epoch [3/5], loss: 0.001693
+Epoch [4/5], loss: 0.001594
+Epoch [5/5], loss: 0.001517
+
+--- Training stage 3/5 for sigma=15/255 ---
+Epoch [1/5], loss: 0.002225
+Epoch [2/5], loss: 0.001117
+Epoch [3/5], loss: 0.001031
+Epoch [4/5], loss: 0.000998
+Epoch [5/5], loss: 0.000963
+
+--- Training stage 4/5 for sigma=15/255 ---
+Epoch [1/5], loss: 0.001716
+Epoch [2/5], loss: 0.001552
+Epoch [3/5], loss: 0.001470
+Epoch [4/5], loss: 0.001424
+Epoch [5/5], loss: 0.001364
+
+--- Training stage 5/5 for sigma=15/255 ---
+Epoch [1/5], loss: 0.001848
+Epoch [2/5], loss: 0.001372
+Epoch [3/5], loss: 0.001315
+Epoch [4/5], loss: 0.001302
+Epoch [5/5], loss: 0.001283
+
+[+] Training complete for sigma=15/255! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 27.96 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma15.pth
diff --git a/logs/overnight_20260408_013608/train_rbf_wave_sigma25.log b/logs/overnight_20260408_013608/train_rbf_wave_sigma25.log
new file mode 100644
index 0000000000000000000000000000000000000000..17c469a2ca12a02c3f40897c9bdd3074c6b20a8a
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_rbf_wave_sigma25.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=25/255...
+
+--- Training stage 1/5 for sigma=25/255 ---
+Epoch [1/5], loss: 0.004493
+Epoch [2/5], loss: 0.001557
+Epoch [3/5], loss: 0.001496
+Epoch [4/5], loss: 0.001453
+Epoch [5/5], loss: 0.001427
+
+--- Training stage 2/5 for sigma=25/255 ---
+Epoch [1/5], loss: 0.006602
+Epoch [2/5], loss: 0.005305
+Epoch [3/5], loss: 0.004850
+Epoch [4/5], loss: 0.004501
+Epoch [5/5], loss: 0.004204
+
+--- Training stage 3/5 for sigma=25/255 ---
+Epoch [1/5], loss: 0.006171
+Epoch [2/5], loss: 0.002156
+Epoch [3/5], loss: 0.002022
+Epoch [4/5], loss: 0.001953
+Epoch [5/5], loss: 0.001917
+
+--- Training stage 4/5 for sigma=25/255 ---
+Epoch [1/5], loss: 0.004317
+Epoch [2/5], loss: 0.003649
+Epoch [3/5], loss: 0.003394
+Epoch [4/5], loss: 0.003182
+Epoch [5/5], loss: 0.003068
+
+--- Training stage 5/5 for sigma=25/255 ---
+Epoch [1/5], loss: 0.004661
+Epoch [2/5], loss: 0.002607
+Epoch [3/5], loss: 0.002485
+Epoch [4/5], loss: 0.002476
+Epoch [5/5], loss: 0.002453
+
+[+] Training complete for sigma=25/255! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 25.51 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma25.pth
diff --git a/logs/overnight_20260408_013608/train_rbf_wave_sigma50.log b/logs/overnight_20260408_013608/train_rbf_wave_sigma50.log
new file mode 100644
index 0000000000000000000000000000000000000000..8359edd178c08435b42cf0af8b5c2ef7a18abb0c
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_rbf_wave_sigma50.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=50/255...
+
+--- Training stage 1/5 for sigma=50/255 ---
+Epoch [1/5], loss: 0.019599
+Epoch [2/5], loss: 0.004185
+Epoch [3/5], loss: 0.003844
+Epoch [4/5], loss: 0.003771
+Epoch [5/5], loss: 0.003700
+
+--- Training stage 2/5 for sigma=50/255 ---
+Epoch [1/5], loss: 0.023277
+Epoch [2/5], loss: 0.018868
+Epoch [3/5], loss: 0.016913
+Epoch [4/5], loss: 0.015442
+Epoch [5/5], loss: 0.014251
+
+--- Training stage 3/5 for sigma=50/255 ---
+Epoch [1/5], loss: 0.025384
+Epoch [2/5], loss: 0.004996
+Epoch [3/5], loss: 0.004653
+Epoch [4/5], loss: 0.004577
+Epoch [5/5], loss: 0.004555
+
+--- Training stage 4/5 for sigma=50/255 ---
+Epoch [1/5], loss: 0.012720
+Epoch [2/5], loss: 0.010063
+Epoch [3/5], loss: 0.009172
+Epoch [4/5], loss: 0.008682
+Epoch [5/5], loss: 0.008198
+
+--- Training stage 5/5 for sigma=50/255 ---
+Epoch [1/5], loss: 0.012618
+Epoch [2/5], loss: 0.005347
+Epoch [3/5], loss: 0.005114
+Epoch [4/5], loss: 0.005116
+Epoch [5/5], loss: 0.005095
+
+[+] Training complete for sigma=50/255! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 22.70 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma50.pth
diff --git a/logs/overnight_20260408_013608/train_rbf_wave_sigma75.log b/logs/overnight_20260408_013608/train_rbf_wave_sigma75.log
new file mode 100644
index 0000000000000000000000000000000000000000..916e4f3f96083d5778d1cf3309a0b2b8be4baa81
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_rbf_wave_sigma75.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=75/255...
+
+--- Training stage 1/5 for sigma=75/255 ---
+Epoch [1/5], loss: 0.038881
+Epoch [2/5], loss: 0.008140
+Epoch [3/5], loss: 0.007280
+Epoch [4/5], loss: 0.006933
+Epoch [5/5], loss: 0.006812
+
+--- Training stage 2/5 for sigma=75/255 ---
+Epoch [1/5], loss: 0.045705
+Epoch [2/5], loss: 0.037601
+Epoch [3/5], loss: 0.033540
+Epoch [4/5], loss: 0.030394
+Epoch [5/5], loss: 0.028022
+
+--- Training stage 3/5 for sigma=75/255 ---
+Epoch [1/5], loss: 0.051907
+Epoch [2/5], loss: 0.008761
+Epoch [3/5], loss: 0.007995
+Epoch [4/5], loss: 0.007693
+Epoch [5/5], loss: 0.007604
+
+--- Training stage 4/5 for sigma=75/255 ---
+Epoch [1/5], loss: 0.023617
+Epoch [2/5], loss: 0.018274
+Epoch [3/5], loss: 0.016900
+Epoch [4/5], loss: 0.015748
+Epoch [5/5], loss: 0.014869
+
+--- Training stage 5/5 for sigma=75/255 ---
+Epoch [1/5], loss: 0.023199
+Epoch [2/5], loss: 0.008684
+Epoch [3/5], loss: 0.008266
+Epoch [4/5], loss: 0.008193
+Epoch [5/5], loss: 0.008121
+
+[+] Training complete for sigma=75/255! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 20.89 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma75.pth
diff --git a/logs/overnight_20260408_013608/train_tnrd_sigma15.log b/logs/overnight_20260408_013608/train_tnrd_sigma15.log
new file mode 100644
index 0000000000000000000000000000000000000000..991edfd1604b416b37cac0991f96dcd98bb259bb
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_tnrd_sigma15.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+
+[*] Initializing 5-Stage TNRD baseline for sigma=15/255...
+
+--- Training TNRD stage 1/5 for sigma=15/255 ---
+Epoch [1/5], loss: 0.000797
+Epoch [2/5], loss: 0.000709
+Epoch [3/5], loss: 0.000723
+Epoch [4/5], loss: 0.000724
+Epoch [5/5], loss: 0.000714
+
+--- Training TNRD stage 2/5 for sigma=15/255 ---
+Epoch [1/5], loss: 0.000706
+Epoch [2/5], loss: 0.000704
+Epoch [3/5], loss: 0.000713
+Epoch [4/5], loss: 0.000706
+Epoch [5/5], loss: 0.000712
+
+--- Training TNRD stage 3/5 for sigma=15/255 ---
+Epoch [1/5], loss: 0.000723
+Epoch [2/5], loss: 0.000722
+Epoch [3/5], loss: 0.000718
+Epoch [4/5], loss: 0.000707
+Epoch [5/5], loss: 0.000716
+
+--- Training TNRD stage 4/5 for sigma=15/255 ---
+Epoch [1/5], loss: 0.000730
+Epoch [2/5], loss: 0.000723
+Epoch [3/5], loss: 0.000728
+Epoch [4/5], loss: 0.000730
+Epoch [5/5], loss: 0.000715
+
+--- Training TNRD stage 5/5 for sigma=15/255 ---
+Epoch [1/5], loss: 0.000739
+Epoch [2/5], loss: 0.000739
+Epoch [3/5], loss: 0.000733
+Epoch [4/5], loss: 0.000740
+Epoch [5/5], loss: 0.000737
+
+[+] Training complete for sigma=15/255! Evaluating on BSD68...
+
+[!!!] Final TNRD baseline PSNR on BSD68 at sigma=15/255: 30.41 dB
+[+] Saved checkpoint: tnrd_baseline_5stages_sigma15.pth
diff --git a/logs/overnight_20260408_013608/train_tnrd_sigma25.log b/logs/overnight_20260408_013608/train_tnrd_sigma25.log
new file mode 100644
index 0000000000000000000000000000000000000000..fccda3d7abe3a5ec50e6ec055132fe6529ce8b08
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_tnrd_sigma25.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+
+[*] Initializing 5-Stage TNRD baseline for sigma=25/255...
+
+--- Training TNRD stage 1/5 for sigma=25/255 ---
+Epoch [1/5], loss: 0.001638
+Epoch [2/5], loss: 0.001414
+Epoch [3/5], loss: 0.001417
+Epoch [4/5], loss: 0.001402
+Epoch [5/5], loss: 0.001395
+
+--- Training TNRD stage 2/5 for sigma=25/255 ---
+Epoch [1/5], loss: 0.001368
+Epoch [2/5], loss: 0.001355
+Epoch [3/5], loss: 0.001354
+Epoch [4/5], loss: 0.001351
+Epoch [5/5], loss: 0.001337
+
+--- Training TNRD stage 3/5 for sigma=25/255 ---
+Epoch [1/5], loss: 0.001379
+Epoch [2/5], loss: 0.001352
+Epoch [3/5], loss: 0.001343
+Epoch [4/5], loss: 0.001354
+Epoch [5/5], loss: 0.001348
+
+--- Training TNRD stage 4/5 for sigma=25/255 ---
+Epoch [1/5], loss: 0.001366
+Epoch [2/5], loss: 0.001356
+Epoch [3/5], loss: 0.001352
+Epoch [4/5], loss: 0.001354
+Epoch [5/5], loss: 0.001359
+
+--- Training TNRD stage 5/5 for sigma=25/255 ---
+Epoch [1/5], loss: 0.001375
+Epoch [2/5], loss: 0.001372
+Epoch [3/5], loss: 0.001372
+Epoch [4/5], loss: 0.001356
+Epoch [5/5], loss: 0.001376
+
+[+] Training complete for sigma=25/255! Evaluating on BSD68...
+
+[!!!] Final TNRD baseline PSNR on BSD68 at sigma=25/255: 27.85 dB
+[+] Saved checkpoint: tnrd_baseline_5stages_sigma25.pth
diff --git a/logs/overnight_20260408_013608/train_tnrd_sigma50.log b/logs/overnight_20260408_013608/train_tnrd_sigma50.log
new file mode 100644
index 0000000000000000000000000000000000000000..8c71e358af28a58e6cd317e4e54f0177b76218ba
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_tnrd_sigma50.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+
+[*] Initializing 5-Stage TNRD baseline for sigma=50/255...
+
+--- Training TNRD stage 1/5 for sigma=50/255 ---
+Epoch [1/5], loss: 0.004549
+Epoch [2/5], loss: 0.003603
+Epoch [3/5], loss: 0.003593
+Epoch [4/5], loss: 0.003555
+Epoch [5/5], loss: 0.003598
+
+--- Training TNRD stage 2/5 for sigma=50/255 ---
+Epoch [1/5], loss: 0.003387
+Epoch [2/5], loss: 0.003352
+Epoch [3/5], loss: 0.003341
+Epoch [4/5], loss: 0.003353
+Epoch [5/5], loss: 0.003324
+
+--- Training TNRD stage 3/5 for sigma=50/255 ---
+Epoch [1/5], loss: 0.003249
+Epoch [2/5], loss: 0.003175
+Epoch [3/5], loss: 0.003178
+Epoch [4/5], loss: 0.003256
+Epoch [5/5], loss: 0.003216
+
+--- Training TNRD stage 4/5 for sigma=50/255 ---
+Epoch [1/5], loss: 0.003265
+Epoch [2/5], loss: 0.003277
+Epoch [3/5], loss: 0.003236
+Epoch [4/5], loss: 0.003231
+Epoch [5/5], loss: 0.003235
+
+--- Training TNRD stage 5/5 for sigma=50/255 ---
+Epoch [1/5], loss: 0.003274
+Epoch [2/5], loss: 0.003171
+Epoch [3/5], loss: 0.003177
+Epoch [4/5], loss: 0.003160
+Epoch [5/5], loss: 0.003193
+
+[+] Training complete for sigma=50/255! Evaluating on BSD68...
+
+[!!!] Final TNRD baseline PSNR on BSD68 at sigma=50/255: 24.58 dB
+[+] Saved checkpoint: tnrd_baseline_5stages_sigma50.pth
diff --git a/logs/overnight_20260408_013608/train_tnrd_sigma75.log b/logs/overnight_20260408_013608/train_tnrd_sigma75.log
new file mode 100644
index 0000000000000000000000000000000000000000..79b129d84b81dd5e2addb7fa8074083106334bd1
--- /dev/null
+++ b/logs/overnight_20260408_013608/train_tnrd_sigma75.log
@@ -0,0 +1,44 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+
+[*] Initializing 5-Stage TNRD baseline for sigma=75/255...
+
+--- Training TNRD stage 1/5 for sigma=75/255 ---
+Epoch [1/5], loss: 0.008659
+Epoch [2/5], loss: 0.006711
+Epoch [3/5], loss: 0.006653
+Epoch [4/5], loss: 0.006685
+Epoch [5/5], loss: 0.006698
+
+--- Training TNRD stage 2/5 for sigma=75/255 ---
+Epoch [1/5], loss: 0.006294
+Epoch [2/5], loss: 0.006209
+Epoch [3/5], loss: 0.006208
+Epoch [4/5], loss: 0.006060
+Epoch [5/5], loss: 0.006121
+
+--- Training TNRD stage 3/5 for sigma=75/255 ---
+Epoch [1/5], loss: 0.005878
+Epoch [2/5], loss: 0.005836
+Epoch [3/5], loss: 0.005894
+Epoch [4/5], loss: 0.005794
+Epoch [5/5], loss: 0.005830
+
+--- Training TNRD stage 4/5 for sigma=75/255 ---
+Epoch [1/5], loss: 0.005976
+Epoch [2/5], loss: 0.005913
+Epoch [3/5], loss: 0.005868
+Epoch [4/5], loss: 0.005862
+Epoch [5/5], loss: 0.005886
+
+--- Training TNRD stage 5/5 for sigma=75/255 ---
+Epoch [1/5], loss: 0.005862
+Epoch [2/5], loss: 0.005818
+Epoch [3/5], loss: 0.005792
+Epoch [4/5], loss: 0.005764
+Epoch [5/5], loss: 0.005836
+
+[+] Training complete for sigma=75/255! Evaluating on BSD68...
+
+[!!!] Final TNRD baseline PSNR on BSD68 at sigma=75/255: 22.42 dB
+[+] Saved checkpoint: tnrd_baseline_5stages_sigma75.pth
diff --git a/logs/overnight_20260408_193023/finetune_mlp_nowave_sigma15_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/finetune_mlp_nowave_sigma15_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..eb5566b2cca813e210bc8de920d60f0bb43ed569
--- /dev/null
+++ b/logs/overnight_20260408_193023/finetune_mlp_nowave_sigma15_gamma0p5_tau0p05.log
@@ -0,0 +1,23 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Loading stage-wise weights for 5-Stage No-wave MLP model at sigma=15/255, gamma=0.5, tau_init=0.05...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.000858
+Fine-tune epoch [2/10], loss: 0.000834
+Fine-tune epoch [3/10], loss: 0.000823
+Fine-tune epoch [4/10], loss: 0.000811
+Fine-tune epoch [5/10], loss: 0.000808
+Fine-tune epoch [6/10], loss: 0.000798
+Fine-tune epoch [7/10], loss: 0.000799
+Fine-tune epoch [8/10], loss: 0.000792
+Fine-tune epoch [9/10], loss: 0.000779
+Fine-tune epoch [10/10], loss: 0.000781
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned No-wave MLP model PSNR (BSD68) at sigma=15/255, gamma=0.5, tau_init=0.05: 30.29 dB
+[+] Saved checkpoint: finetuned_5stages_waveFalse_sigma15_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/finetune_mlp_nowave_sigma15_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/finetune_mlp_nowave_sigma15_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..c9aa70a5c8e3c79386f8ac12a703c06c7da2df1c
--- /dev/null
+++ b/logs/overnight_20260408_193023/finetune_mlp_nowave_sigma15_gamma0p5_tau0p1.log
@@ -0,0 +1,23 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Loading stage-wise weights for 5-Stage No-wave MLP model at sigma=15/255, gamma=0.5, tau_init=0.1...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.000918
+Fine-tune epoch [2/10], loss: 0.000906
+Fine-tune epoch [3/10], loss: 0.000889
+Fine-tune epoch [4/10], loss: 0.000888
+Fine-tune epoch [5/10], loss: 0.000874
+Fine-tune epoch [6/10], loss: 0.000860
+Fine-tune epoch [7/10], loss: 0.000849
+Fine-tune epoch [8/10], loss: 0.000840
+Fine-tune epoch [9/10], loss: 0.000833
+Fine-tune epoch [10/10], loss: 0.000835
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned No-wave MLP model PSNR (BSD68) at sigma=15/255, gamma=0.5, tau_init=0.1: 30.07 dB
+[+] Saved checkpoint: finetuned_5stages_waveFalse_sigma15_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/finetune_mlp_nowave_sigma15_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/finetune_mlp_nowave_sigma15_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..25631ff0dff72077f61f71741da866744f3065cc
--- /dev/null
+++ b/logs/overnight_20260408_193023/finetune_mlp_nowave_sigma15_gamma0p5_tau0p2.log
@@ -0,0 +1,23 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Loading stage-wise weights for 5-Stage No-wave MLP model at sigma=15/255, gamma=0.5, tau_init=0.2...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.000972
+Fine-tune epoch [2/10], loss: 0.000919
+Fine-tune epoch [3/10], loss: 0.000879
+Fine-tune epoch [4/10], loss: 0.000872
+Fine-tune epoch [5/10], loss: 0.000861
+Fine-tune epoch [6/10], loss: 0.000850
+Fine-tune epoch [7/10], loss: 0.000851
+Fine-tune epoch [8/10], loss: 0.000836
+Fine-tune epoch [9/10], loss: 0.000832
+Fine-tune epoch [10/10], loss: 0.000821
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned No-wave MLP model PSNR (BSD68) at sigma=15/255, gamma=0.5, tau_init=0.2: 30.08 dB
+[+] Saved checkpoint: finetuned_5stages_waveFalse_sigma15_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/finetune_mlp_nowave_sigma15_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/finetune_mlp_nowave_sigma15_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..b21b345b712f3999363f127e7dbc59a41d755374
--- /dev/null
+++ b/logs/overnight_20260408_193023/finetune_mlp_nowave_sigma15_gamma1p0_tau0p1.log
@@ -0,0 +1,23 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Loading stage-wise weights for 5-Stage No-wave MLP model at sigma=15/255, gamma=1.0, tau_init=0.1...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.000914
+Fine-tune epoch [2/10], loss: 0.000882
+Fine-tune epoch [3/10], loss: 0.000878
+Fine-tune epoch [4/10], loss: 0.000869
+Fine-tune epoch [5/10], loss: 0.000850
+Fine-tune epoch [6/10], loss: 0.000840
+Fine-tune epoch [7/10], loss: 0.000840
+Fine-tune epoch [8/10], loss: 0.000832
+Fine-tune epoch [9/10], loss: 0.000820
+Fine-tune epoch [10/10], loss: 0.000822
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned No-wave MLP model PSNR (BSD68) at sigma=15/255, gamma=1.0, tau_init=0.1: 30.11 dB
+[+] Saved checkpoint: finetuned_5stages_waveFalse_sigma15_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/finetune_mlp_wave_sigma15_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/finetune_mlp_wave_sigma15_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..f2cf34b1382e13e7c0fbf1efdd1191b5b096066a
--- /dev/null
+++ b/logs/overnight_20260408_193023/finetune_mlp_wave_sigma15_gamma0p5_tau0p05.log
@@ -0,0 +1,23 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Loading stage-wise weights for 5-Stage Telegraph MLP model at sigma=15/255, gamma=0.5, tau_init=0.05...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.002542
+Fine-tune epoch [2/10], loss: 0.002186
+Fine-tune epoch [3/10], loss: 0.001891
+Fine-tune epoch [4/10], loss: 0.001763
+Fine-tune epoch [5/10], loss: 0.001645
+Fine-tune epoch [6/10], loss: 0.001547
+Fine-tune epoch [7/10], loss: 0.001469
+Fine-tune epoch [8/10], loss: 0.001360
+Fine-tune epoch [9/10], loss: 0.001301
+Fine-tune epoch [10/10], loss: 0.001246
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned Telegraph MLP model PSNR (BSD68) at sigma=15/255, gamma=0.5, tau_init=0.05: 28.71 dB
+[+] Saved checkpoint: finetuned_5stages_waveTrue_sigma15_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/finetune_mlp_wave_sigma15_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/finetune_mlp_wave_sigma15_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..7a833fe9581275589df48839b3bbe572edfb35f7
--- /dev/null
+++ b/logs/overnight_20260408_193023/finetune_mlp_wave_sigma15_gamma0p5_tau0p1.log
@@ -0,0 +1,23 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Loading stage-wise weights for 5-Stage Telegraph MLP model at sigma=15/255, gamma=0.5, tau_init=0.1...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.001550
+Fine-tune epoch [2/10], loss: 0.001376
+Fine-tune epoch [3/10], loss: 0.001269
+Fine-tune epoch [4/10], loss: 0.001177
+Fine-tune epoch [5/10], loss: 0.001148
+Fine-tune epoch [6/10], loss: 0.001138
+Fine-tune epoch [7/10], loss: 0.001092
+Fine-tune epoch [8/10], loss: 0.001066
+Fine-tune epoch [9/10], loss: 0.001033
+Fine-tune epoch [10/10], loss: 0.001019
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned Telegraph MLP model PSNR (BSD68) at sigma=15/255, gamma=0.5, tau_init=0.1: 29.29 dB
+[+] Saved checkpoint: finetuned_5stages_waveTrue_sigma15_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/finetune_mlp_wave_sigma15_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/finetune_mlp_wave_sigma15_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..9cdcd382f29c9efc728912e453cf2b62d0e99c4d
--- /dev/null
+++ b/logs/overnight_20260408_193023/finetune_mlp_wave_sigma15_gamma0p5_tau0p2.log
@@ -0,0 +1,23 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Loading stage-wise weights for 5-Stage Telegraph MLP model at sigma=15/255, gamma=0.5, tau_init=0.2...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.001579
+Fine-tune epoch [2/10], loss: 0.001342
+Fine-tune epoch [3/10], loss: 0.001251
+Fine-tune epoch [4/10], loss: 0.001154
+Fine-tune epoch [5/10], loss: 0.001103
+Fine-tune epoch [6/10], loss: 0.001092
+Fine-tune epoch [7/10], loss: 0.001063
+Fine-tune epoch [8/10], loss: 0.001039
+Fine-tune epoch [9/10], loss: 0.001038
+Fine-tune epoch [10/10], loss: 0.001019
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned Telegraph MLP model PSNR (BSD68) at sigma=15/255, gamma=0.5, tau_init=0.2: 29.29 dB
+[+] Saved checkpoint: finetuned_5stages_waveTrue_sigma15_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/finetune_mlp_wave_sigma15_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/finetune_mlp_wave_sigma15_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..688bea7c3e0cf17155c7cfdde23f09ce8f7a7459
--- /dev/null
+++ b/logs/overnight_20260408_193023/finetune_mlp_wave_sigma15_gamma1p0_tau0p1.log
@@ -0,0 +1,23 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Loading stage-wise weights for 5-Stage Telegraph MLP model at sigma=15/255, gamma=1.0, tau_init=0.1...
+
+--- Starting end-to-end fine-tuning ---
+Fine-tune epoch [1/10], loss: 0.001320
+Fine-tune epoch [2/10], loss: 0.001196
+Fine-tune epoch [3/10], loss: 0.001100
+Fine-tune epoch [4/10], loss: 0.001112
+Fine-tune epoch [5/10], loss: 0.001062
+Fine-tune epoch [6/10], loss: 0.001040
+Fine-tune epoch [7/10], loss: 0.001035
+Fine-tune epoch [8/10], loss: 0.001014
+Fine-tune epoch [9/10], loss: 0.001009
+Fine-tune epoch [10/10], loss: 0.000985
+
+[+] Fine-tuning complete! Evaluating...
+
+[!!!] Fine-tuned Telegraph MLP model PSNR (BSD68) at sigma=15/255, gamma=1.0, tau_init=0.1: 29.44 dB
+[+] Saved checkpoint: finetuned_5stages_waveTrue_sigma15_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/finetune_rbf_nowave_sigma15_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/finetune_rbf_nowave_sigma15_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..237613caee36acfec3cbf7dc9885dd646701f816
--- /dev/null
+++ b/logs/overnight_20260408_193023/finetune_rbf_nowave_sigma15_gamma0p5_tau0p05.log
@@ -0,0 +1,47 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Loading stage-wise weights for 5-Stage No-wave RBF model at sigma=15/255, gamma=0.5, tau_init=0.05...
+
+--- Starting end-to-end fine-tuning ---
+Traceback (most recent call last):
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 247, in <module>
+    fine_tune(args)
+    ~~~~~~~~~^^^^^^
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 197, in fine_tune
+    fine_tune_single_config(
+    ~~~~~~~~~~~~~~~~~~~~~~~^
+        args, sigma_value, damping_gamma, tau_init, train_root, test_root
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 132, in fine_tune_single_config
+    scaler.scale(loss).backward()
+    ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/_tensor.py", line 631, in backward
+    torch.autograd.backward(
+    ~~~~~~~~~~~~~~~~~~~~~~~^
+        self, gradient, retain_graph, create_graph, inputs=inputs
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/autograd/__init__.py", line 381, in backward
+    _engine_run_backward(
+    ~~~~~~~~~~~~~~~~~~~~^
+        tensors,
+        ^^^^^^^^
+    ...<5 lines>...
+        accumulate_grad=True,
+        ^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/autograd/graph.py", line 869, in _engine_run_backward
+    return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+        t_outputs, *args, **kwargs
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )  # Calls into the C++ engine to run the backward pass
+    ^
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.91 GiB. GPU 0 has a total capacity of 79.18 GiB of which 892.06 MiB is free. Process 2423809 has 42.73 GiB memory in use. Including non-PyTorch memory, this process has 35.56 GiB memory in use. Of the allocated memory 32.29 GiB is allocated by PyTorch, and 2.66 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)
diff --git a/logs/overnight_20260408_193023/finetune_rbf_nowave_sigma15_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/finetune_rbf_nowave_sigma15_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..cdf8817375ffe7e296a8ace2d6f2e6e3acb24fc4
--- /dev/null
+++ b/logs/overnight_20260408_193023/finetune_rbf_nowave_sigma15_gamma0p5_tau0p2.log
@@ -0,0 +1,44 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Loading stage-wise weights for 5-Stage No-wave RBF model at sigma=15/255, gamma=0.5, tau_init=0.2...
+
+--- Starting end-to-end fine-tuning ---
+Traceback (most recent call last):
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 247, in <module>
+    fine_tune(args)
+    ~~~~~~~~~^^^^^^
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 197, in fine_tune
+    fine_tune_single_config(
+    ~~~~~~~~~~~~~~~~~~~~~~~^
+        args, sigma_value, damping_gamma, tau_init, train_root, test_root
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 129, in fine_tune_single_config
+    output = model(noisy)
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/train_network_rbf.py", line 106, in forward
+    u_next = stage(u_n, u_n_minus_1)
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/train_network_rbf.py", line 71, in forward
+    diffused = self.phi(edges)
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/train_network_rbf.py", line 51, in forward
+    basis_vals = torch.exp(-self.gamma * (x_expanded - self.centers) ** 2)
+                           ~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.91 GiB. GPU 0 has a total capacity of 79.18 GiB of which 2.56 GiB is free. Process 2423811 has 43.91 GiB memory in use. Including non-PyTorch memory, this process has 32.70 GiB memory in use. Of the allocated memory 29.33 GiB is allocated by PyTorch, and 2.76 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)
diff --git a/logs/overnight_20260408_193023/finetune_rbf_wave_sigma15_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/finetune_rbf_wave_sigma15_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..d95be5cb44dd0072ca78a64b26e84e4b1dec7174
--- /dev/null
+++ b/logs/overnight_20260408_193023/finetune_rbf_wave_sigma15_gamma0p5_tau0p05.log
@@ -0,0 +1,44 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Loading stage-wise weights for 5-Stage Telegraph RBF model at sigma=15/255, gamma=0.5, tau_init=0.05...
+
+--- Starting end-to-end fine-tuning ---
+Traceback (most recent call last):
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 247, in <module>
+    fine_tune(args)
+    ~~~~~~~~~^^^^^^
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 197, in fine_tune
+    fine_tune_single_config(
+    ~~~~~~~~~~~~~~~~~~~~~~~^
+        args, sigma_value, damping_gamma, tau_init, train_root, test_root
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 129, in fine_tune_single_config
+    output = model(noisy)
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/train_network_rbf.py", line 106, in forward
+    u_next = stage(u_n, u_n_minus_1)
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/train_network_rbf.py", line 71, in forward
+    diffused = self.phi(edges)
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/train_network_rbf.py", line 51, in forward
+    basis_vals = torch.exp(-self.gamma * (x_expanded - self.centers) ** 2)
+                           ~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.91 GiB. GPU 0 has a total capacity of 79.18 GiB of which 2.86 GiB is free. Process 2423808 has 43.61 GiB memory in use. Including non-PyTorch memory, this process has 32.70 GiB memory in use. Of the allocated memory 29.34 GiB is allocated by PyTorch, and 2.75 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)
diff --git a/logs/overnight_20260408_193023/finetune_rbf_wave_sigma15_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/finetune_rbf_wave_sigma15_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..9f676399648034e269ccd90bc299dd19172223d4
--- /dev/null
+++ b/logs/overnight_20260408_193023/finetune_rbf_wave_sigma15_gamma0p5_tau0p2.log
@@ -0,0 +1,47 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Loading stage-wise weights for 5-Stage Telegraph RBF model at sigma=15/255, gamma=0.5, tau_init=0.2...
+
+--- Starting end-to-end fine-tuning ---
+Traceback (most recent call last):
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 247, in <module>
+    fine_tune(args)
+    ~~~~~~~~~^^^^^^
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 197, in fine_tune
+    fine_tune_single_config(
+    ~~~~~~~~~~~~~~~~~~~~~~~^
+        args, sigma_value, damping_gamma, tau_init, train_root, test_root
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 132, in fine_tune_single_config
+    scaler.scale(loss).backward()
+    ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/_tensor.py", line 631, in backward
+    torch.autograd.backward(
+    ~~~~~~~~~~~~~~~~~~~~~~~^
+        self, gradient, retain_graph, create_graph, inputs=inputs
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/autograd/__init__.py", line 381, in backward
+    _engine_run_backward(
+    ~~~~~~~~~~~~~~~~~~~~^
+        tensors,
+        ^^^^^^^^
+    ...<5 lines>...
+        accumulate_grad=True,
+        ^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/autograd/graph.py", line 869, in _engine_run_backward
+    return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+        t_outputs, *args, **kwargs
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )  # Calls into the C++ engine to run the backward pass
+    ^
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.91 GiB. GPU 0 has a total capacity of 79.18 GiB of which 38.06 MiB is free. Process 2423806 has 43.52 GiB memory in use. Including non-PyTorch memory, this process has 35.61 GiB memory in use. Of the allocated memory 32.30 GiB is allocated by PyTorch, and 2.69 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)
diff --git a/logs/overnight_20260408_193023/finetune_rbf_wave_sigma15_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/finetune_rbf_wave_sigma15_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..cd4c4e1ba65bf79778eeb562ee6d4f3cfa015de6
--- /dev/null
+++ b/logs/overnight_20260408_193023/finetune_rbf_wave_sigma15_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Loading stage-wise weights for 5-Stage Telegraph RBF model at sigma=15/255, gamma=1.0, tau_init=0.05...
+
+--- Starting end-to-end fine-tuning ---
+Traceback (most recent call last):
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 247, in <module>
+    fine_tune(args)
+    ~~~~~~~~~^^^^^^
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 197, in fine_tune
+    fine_tune_single_config(
+    ~~~~~~~~~~~~~~~~~~~~~~~^
+        args, sigma_value, damping_gamma, tau_init, train_root, test_root
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 129, in fine_tune_single_config
+    output = model(noisy)
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/train_network_rbf.py", line 106, in forward
+    u_next = stage(u_n, u_n_minus_1)
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/train_network_rbf.py", line 71, in forward
+    diffused = self.phi(edges)
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/train_network_rbf.py", line 51, in forward
+    basis_vals = torch.exp(-self.gamma * (x_expanded - self.centers) ** 2)
+                                         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^~~
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/_tensor.py", line 47, in wrapped
+    return f(self, *args, **kwargs)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.91 GiB. GPU 0 has a total capacity of 79.18 GiB of which 2.71 GiB is free. Process 2423810 has 52.48 GiB memory in use. Including non-PyTorch memory, this process has 23.98 GiB memory in use. Of the allocated memory 20.56 GiB is allocated by PyTorch, and 2.80 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)
diff --git a/logs/overnight_20260408_193023/finetune_rbf_wave_sigma15_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/finetune_rbf_wave_sigma15_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..151e3d9518acff9b7f7f4788889c0ae767952436
--- /dev/null
+++ b/logs/overnight_20260408_193023/finetune_rbf_wave_sigma15_gamma1p0_tau0p1.log
@@ -0,0 +1,47 @@
+[*] Fine-tune data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Loading stage-wise weights for 5-Stage Telegraph RBF model at sigma=15/255, gamma=1.0, tau_init=0.1...
+
+--- Starting end-to-end fine-tuning ---
+Traceback (most recent call last):
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 247, in <module>
+    fine_tune(args)
+    ~~~~~~~~~^^^^^^
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 197, in fine_tune
+    fine_tune_single_config(
+    ~~~~~~~~~~~~~~~~~~~~~~~^
+        args, sigma_value, damping_gamma, tau_init, train_root, test_root
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/finetune.py", line 132, in fine_tune_single_config
+    scaler.scale(loss).backward()
+    ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/_tensor.py", line 631, in backward
+    torch.autograd.backward(
+    ~~~~~~~~~~~~~~~~~~~~~~~^
+        self, gradient, retain_graph, create_graph, inputs=inputs
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/autograd/__init__.py", line 381, in backward
+    _engine_run_backward(
+    ~~~~~~~~~~~~~~~~~~~~^
+        tensors,
+        ^^^^^^^^
+    ...<5 lines>...
+        accumulate_grad=True,
+        ^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/home/llmteam/miniconda/envs/sidharth/lib/python3.13/site-packages/torch/autograd/graph.py", line 869, in _engine_run_backward
+    return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+        t_outputs, *args, **kwargs
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )  # Calls into the C++ engine to run the backward pass
+    ^
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.91 GiB. GPU 0 has a total capacity of 79.18 GiB of which 802.06 MiB is free. Process 2423807 has 42.77 GiB memory in use. Including non-PyTorch memory, this process has 35.61 GiB memory in use. Of the allocated memory 32.30 GiB is allocated by PyTorch, and 2.69 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..b7b4a225cfc63db81773b394378274ee693caeed
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma0p5_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage No-wave Model for sigma=15/255, gamma=0.5, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.003068
+Epoch [2/5], Loss: 0.002108
+Epoch [3/5], Loss: 0.001558
+Epoch [4/5], Loss: 0.001238
+Epoch [5/5], Loss: 0.001099
+
+--- Training Stage 2/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001160
+Epoch [2/5], Loss: 0.001080
+Epoch [3/5], Loss: 0.001029
+Epoch [4/5], Loss: 0.001009
+Epoch [5/5], Loss: 0.000978
+
+--- Training Stage 3/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001168
+Epoch [2/5], Loss: 0.001024
+Epoch [3/5], Loss: 0.000975
+Epoch [4/5], Loss: 0.000959
+Epoch [5/5], Loss: 0.000939
+
+--- Training Stage 4/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001156
+Epoch [2/5], Loss: 0.000970
+Epoch [3/5], Loss: 0.000926
+Epoch [4/5], Loss: 0.000918
+Epoch [5/5], Loss: 0.000904
+
+--- Training Stage 5/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001041
+Epoch [2/5], Loss: 0.000935
+Epoch [3/5], Loss: 0.000920
+Epoch [4/5], Loss: 0.000900
+Epoch [5/5], Loss: 0.000888
+
+[+] Training complete for sigma=15/255, gamma=0.5, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=0.5, tau_init=0.05: 29.87 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma15_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..a5b268ba61984035eddd2edd61ef948dca28b3b7
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma0p5_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage No-wave Model for sigma=15/255, gamma=0.5, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.003030
+Epoch [2/5], Loss: 0.001761
+Epoch [3/5], Loss: 0.001425
+Epoch [4/5], Loss: 0.001296
+Epoch [5/5], Loss: 0.001224
+
+--- Training Stage 2/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.001891
+Epoch [2/5], Loss: 0.001374
+Epoch [3/5], Loss: 0.001227
+Epoch [4/5], Loss: 0.001151
+Epoch [5/5], Loss: 0.001106
+
+--- Training Stage 3/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.001911
+Epoch [2/5], Loss: 0.001298
+Epoch [3/5], Loss: 0.001136
+Epoch [4/5], Loss: 0.001081
+Epoch [5/5], Loss: 0.001058
+
+--- Training Stage 4/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.001466
+Epoch [2/5], Loss: 0.001159
+Epoch [3/5], Loss: 0.001081
+Epoch [4/5], Loss: 0.001028
+Epoch [5/5], Loss: 0.000999
+
+--- Training Stage 5/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.001420
+Epoch [2/5], Loss: 0.001064
+Epoch [3/5], Loss: 0.000978
+Epoch [4/5], Loss: 0.000945
+Epoch [5/5], Loss: 0.000934
+
+[+] Training complete for sigma=15/255, gamma=0.5, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=0.5, tau_init=0.1: 29.61 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma15_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..2cb9a3141eaacdc2f1e35a6547b53407356eb7f1
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma0p5_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage No-wave Model for sigma=15/255, gamma=0.5, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.006745
+Epoch [2/5], Loss: 0.002817
+Epoch [3/5], Loss: 0.001759
+Epoch [4/5], Loss: 0.001355
+Epoch [5/5], Loss: 0.001182
+
+--- Training Stage 2/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.005872
+Epoch [2/5], Loss: 0.002904
+Epoch [3/5], Loss: 0.001917
+Epoch [4/5], Loss: 0.001480
+Epoch [5/5], Loss: 0.001247
+
+--- Training Stage 3/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.009354
+Epoch [2/5], Loss: 0.004736
+Epoch [3/5], Loss: 0.002833
+Epoch [4/5], Loss: 0.001971
+Epoch [5/5], Loss: 0.001574
+
+--- Training Stage 4/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.004804
+Epoch [2/5], Loss: 0.002111
+Epoch [3/5], Loss: 0.001341
+Epoch [4/5], Loss: 0.001164
+Epoch [5/5], Loss: 0.001095
+
+--- Training Stage 5/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.002164
+Epoch [2/5], Loss: 0.001284
+Epoch [3/5], Loss: 0.001114
+Epoch [4/5], Loss: 0.001046
+Epoch [5/5], Loss: 0.001027
+
+[+] Training complete for sigma=15/255, gamma=0.5, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=0.5, tau_init=0.2: 29.39 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma15_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..36a037d99e082cbd6fbb460887e8471a2cd1d708
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage No-wave Model for sigma=15/255, gamma=1.0, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.002897
+Epoch [2/5], Loss: 0.001984
+Epoch [3/5], Loss: 0.001474
+Epoch [4/5], Loss: 0.001282
+Epoch [5/5], Loss: 0.001208
+
+--- Training Stage 2/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001276
+Epoch [2/5], Loss: 0.001170
+Epoch [3/5], Loss: 0.001131
+Epoch [4/5], Loss: 0.001092
+Epoch [5/5], Loss: 0.001070
+
+--- Training Stage 3/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001557
+Epoch [2/5], Loss: 0.001249
+Epoch [3/5], Loss: 0.001129
+Epoch [4/5], Loss: 0.001087
+Epoch [5/5], Loss: 0.001066
+
+--- Training Stage 4/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001270
+Epoch [2/5], Loss: 0.001079
+Epoch [3/5], Loss: 0.001037
+Epoch [4/5], Loss: 0.001032
+Epoch [5/5], Loss: 0.001007
+
+--- Training Stage 5/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001193
+Epoch [2/5], Loss: 0.001075
+Epoch [3/5], Loss: 0.001030
+Epoch [4/5], Loss: 0.001015
+Epoch [5/5], Loss: 0.000991
+
+[+] Training complete for sigma=15/255, gamma=1.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=1.0, tau_init=0.05: 29.49 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma15_gamma1_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..15ed8c21fd27e46958235130ef0fe849c07f84a2
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma1p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage No-wave Model for sigma=15/255, gamma=1.0, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.004462
+Epoch [2/5], Loss: 0.002231
+Epoch [3/5], Loss: 0.001528
+Epoch [4/5], Loss: 0.001285
+Epoch [5/5], Loss: 0.001183
+
+--- Training Stage 2/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.002211
+Epoch [2/5], Loss: 0.001409
+Epoch [3/5], Loss: 0.001187
+Epoch [4/5], Loss: 0.001114
+Epoch [5/5], Loss: 0.001075
+
+--- Training Stage 3/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.003194
+Epoch [2/5], Loss: 0.001869
+Epoch [3/5], Loss: 0.001387
+Epoch [4/5], Loss: 0.001203
+Epoch [5/5], Loss: 0.001110
+
+--- Training Stage 4/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.001958
+Epoch [2/5], Loss: 0.001342
+Epoch [3/5], Loss: 0.001120
+Epoch [4/5], Loss: 0.001033
+Epoch [5/5], Loss: 0.000978
+
+--- Training Stage 5/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.001639
+Epoch [2/5], Loss: 0.001147
+Epoch [3/5], Loss: 0.000996
+Epoch [4/5], Loss: 0.000952
+Epoch [5/5], Loss: 0.000935
+
+[+] Training complete for sigma=15/255, gamma=1.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=1.0, tau_init=0.1: 29.70 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma15_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma1p0_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma1p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..ab53629e0387a00668eb0fb75e28c323a73f4be7
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma1p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage No-wave Model for sigma=15/255, gamma=1.0, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.005635
+Epoch [2/5], Loss: 0.002158
+Epoch [3/5], Loss: 0.001481
+Epoch [4/5], Loss: 0.001295
+Epoch [5/5], Loss: 0.001250
+
+--- Training Stage 2/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.003467
+Epoch [2/5], Loss: 0.001960
+Epoch [3/5], Loss: 0.001511
+Epoch [4/5], Loss: 0.001301
+Epoch [5/5], Loss: 0.001194
+
+--- Training Stage 3/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.004285
+Epoch [2/5], Loss: 0.002098
+Epoch [3/5], Loss: 0.001519
+Epoch [4/5], Loss: 0.001287
+Epoch [5/5], Loss: 0.001192
+
+--- Training Stage 4/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.006800
+Epoch [2/5], Loss: 0.003628
+Epoch [3/5], Loss: 0.002410
+Epoch [4/5], Loss: 0.001830
+Epoch [5/5], Loss: 0.001503
+
+--- Training Stage 5/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.002916
+Epoch [2/5], Loss: 0.001783
+Epoch [3/5], Loss: 0.001349
+Epoch [4/5], Loss: 0.001151
+Epoch [5/5], Loss: 0.001080
+
+[+] Training complete for sigma=15/255, gamma=1.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=1.0, tau_init=0.2: 29.28 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma15_gamma1_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma2p0_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma2p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..79be7ea60fb2002fa8a4f36169c9684337bc2d99
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma2p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage No-wave Model for sigma=15/255, gamma=2.0, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.003663
+Epoch [2/5], Loss: 0.002544
+Epoch [3/5], Loss: 0.001836
+Epoch [4/5], Loss: 0.001479
+Epoch [5/5], Loss: 0.001314
+
+--- Training Stage 2/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001519
+Epoch [2/5], Loss: 0.001322
+Epoch [3/5], Loss: 0.001259
+Epoch [4/5], Loss: 0.001204
+Epoch [5/5], Loss: 0.001165
+
+--- Training Stage 3/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001239
+Epoch [2/5], Loss: 0.001189
+Epoch [3/5], Loss: 0.001162
+Epoch [4/5], Loss: 0.001152
+Epoch [5/5], Loss: 0.001120
+
+--- Training Stage 4/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001165
+Epoch [2/5], Loss: 0.001099
+Epoch [3/5], Loss: 0.001077
+Epoch [4/5], Loss: 0.001058
+Epoch [5/5], Loss: 0.001030
+
+--- Training Stage 5/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001107
+Epoch [2/5], Loss: 0.001023
+Epoch [3/5], Loss: 0.001004
+Epoch [4/5], Loss: 0.000999
+Epoch [5/5], Loss: 0.000986
+
+[+] Training complete for sigma=15/255, gamma=2.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=2.0, tau_init=0.05: 29.44 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma15_gamma2_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma2p0_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma2p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..226a9d542a728bdc8e3560d6c1e3fae057696201
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma2p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage No-wave Model for sigma=15/255, gamma=2.0, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.003318
+Epoch [2/5], Loss: 0.002041
+Epoch [3/5], Loss: 0.001590
+Epoch [4/5], Loss: 0.001348
+Epoch [5/5], Loss: 0.001203
+
+--- Training Stage 2/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.001493
+Epoch [2/5], Loss: 0.001206
+Epoch [3/5], Loss: 0.001127
+Epoch [4/5], Loss: 0.001071
+Epoch [5/5], Loss: 0.001061
+
+--- Training Stage 3/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.002057
+Epoch [2/5], Loss: 0.001428
+Epoch [3/5], Loss: 0.001176
+Epoch [4/5], Loss: 0.001065
+Epoch [5/5], Loss: 0.001015
+
+--- Training Stage 4/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.001586
+Epoch [2/5], Loss: 0.001163
+Epoch [3/5], Loss: 0.001044
+Epoch [4/5], Loss: 0.000982
+Epoch [5/5], Loss: 0.000961
+
+--- Training Stage 5/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.001295
+Epoch [2/5], Loss: 0.001014
+Epoch [3/5], Loss: 0.000944
+Epoch [4/5], Loss: 0.000910
+Epoch [5/5], Loss: 0.000887
+
+[+] Training complete for sigma=15/255, gamma=2.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=2.0, tau_init=0.1: 29.82 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma15_gamma2_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma2p0_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma2p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..bfecc99933d58115486d4dc17773f9259bd5ccde
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma15_gamma2p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage No-wave Model for sigma=15/255, gamma=2.0, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.004980
+Epoch [2/5], Loss: 0.002125
+Epoch [3/5], Loss: 0.001492
+Epoch [4/5], Loss: 0.001296
+Epoch [5/5], Loss: 0.001222
+
+--- Training Stage 2/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.007784
+Epoch [2/5], Loss: 0.003635
+Epoch [3/5], Loss: 0.002306
+Epoch [4/5], Loss: 0.001759
+Epoch [5/5], Loss: 0.001486
+
+--- Training Stage 3/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.006520
+Epoch [2/5], Loss: 0.003505
+Epoch [3/5], Loss: 0.002164
+Epoch [4/5], Loss: 0.001455
+Epoch [5/5], Loss: 0.001209
+
+--- Training Stage 4/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.003349
+Epoch [2/5], Loss: 0.002029
+Epoch [3/5], Loss: 0.001534
+Epoch [4/5], Loss: 0.001230
+Epoch [5/5], Loss: 0.001046
+
+--- Training Stage 5/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.002599
+Epoch [2/5], Loss: 0.001427
+Epoch [3/5], Loss: 0.001177
+Epoch [4/5], Loss: 0.001082
+Epoch [5/5], Loss: 0.001026
+
+[+] Training complete for sigma=15/255, gamma=2.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=2.0, tau_init=0.2: 29.44 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma15_gamma2_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..722d66f8097160717c14cdbc06292e057bf87554
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma0p5_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage No-wave Model for sigma=25/255, gamma=0.5, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.005912
+Epoch [2/5], Loss: 0.003352
+Epoch [3/5], Loss: 0.002489
+Epoch [4/5], Loss: 0.002177
+Epoch [5/5], Loss: 0.002043
+
+--- Training Stage 2/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.002077
+Epoch [2/5], Loss: 0.001959
+Epoch [3/5], Loss: 0.001902
+Epoch [4/5], Loss: 0.001882
+Epoch [5/5], Loss: 0.001841
+
+--- Training Stage 3/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001875
+Epoch [2/5], Loss: 0.001786
+Epoch [3/5], Loss: 0.001775
+Epoch [4/5], Loss: 0.001736
+Epoch [5/5], Loss: 0.001698
+
+--- Training Stage 4/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001726
+Epoch [2/5], Loss: 0.001671
+Epoch [3/5], Loss: 0.001633
+Epoch [4/5], Loss: 0.001626
+Epoch [5/5], Loss: 0.001588
+
+--- Training Stage 5/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001799
+Epoch [2/5], Loss: 0.001643
+Epoch [3/5], Loss: 0.001610
+Epoch [4/5], Loss: 0.001611
+Epoch [5/5], Loss: 0.001595
+
+[+] Training complete for sigma=25/255, gamma=0.5, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=0.5, tau_init=0.05: 27.39 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma25_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..31de837fed1bf46ecec4c24fc2a97044791024fb
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma0p5_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage No-wave Model for sigma=25/255, gamma=0.5, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.007215
+Epoch [2/5], Loss: 0.003998
+Epoch [3/5], Loss: 0.002868
+Epoch [4/5], Loss: 0.002456
+Epoch [5/5], Loss: 0.002267
+
+--- Training Stage 2/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.003494
+Epoch [2/5], Loss: 0.002734
+Epoch [3/5], Loss: 0.002375
+Epoch [4/5], Loss: 0.002228
+Epoch [5/5], Loss: 0.002096
+
+--- Training Stage 3/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.003027
+Epoch [2/5], Loss: 0.002334
+Epoch [3/5], Loss: 0.002056
+Epoch [4/5], Loss: 0.001930
+Epoch [5/5], Loss: 0.001881
+
+--- Training Stage 4/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.002053
+Epoch [2/5], Loss: 0.001867
+Epoch [3/5], Loss: 0.001780
+Epoch [4/5], Loss: 0.001710
+Epoch [5/5], Loss: 0.001708
+
+--- Training Stage 5/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.002104
+Epoch [2/5], Loss: 0.001776
+Epoch [3/5], Loss: 0.001702
+Epoch [4/5], Loss: 0.001646
+Epoch [5/5], Loss: 0.001611
+
+[+] Training complete for sigma=25/255, gamma=0.5, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=0.5, tau_init=0.1: 27.36 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma25_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..1d8afa46020f7ee57847f2523b3446f846083167
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma0p5_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage No-wave Model for sigma=25/255, gamma=0.5, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.023670
+Epoch [2/5], Loss: 0.011184
+Epoch [3/5], Loss: 0.006820
+Epoch [4/5], Loss: 0.004415
+Epoch [5/5], Loss: 0.003546
+
+--- Training Stage 2/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.008096
+Epoch [2/5], Loss: 0.004298
+Epoch [3/5], Loss: 0.002948
+Epoch [4/5], Loss: 0.002460
+Epoch [5/5], Loss: 0.002237
+
+--- Training Stage 3/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.003611
+Epoch [2/5], Loss: 0.002578
+Epoch [3/5], Loss: 0.002236
+Epoch [4/5], Loss: 0.002064
+Epoch [5/5], Loss: 0.001966
+
+--- Training Stage 4/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.010230
+Epoch [2/5], Loss: 0.005058
+Epoch [3/5], Loss: 0.003306
+Epoch [4/5], Loss: 0.002549
+Epoch [5/5], Loss: 0.002193
+
+--- Training Stage 5/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.006349
+Epoch [2/5], Loss: 0.003012
+Epoch [3/5], Loss: 0.002083
+Epoch [4/5], Loss: 0.001825
+Epoch [5/5], Loss: 0.001779
+
+[+] Training complete for sigma=25/255, gamma=0.5, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=0.5, tau_init=0.2: 27.02 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma25_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..3d067805bfeea0228eb1a6ce1b64fb9d54553f74
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage No-wave Model for sigma=25/255, gamma=1.0, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.007555
+Epoch [2/5], Loss: 0.004493
+Epoch [3/5], Loss: 0.002951
+Epoch [4/5], Loss: 0.002491
+Epoch [5/5], Loss: 0.002254
+
+--- Training Stage 2/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.002296
+Epoch [2/5], Loss: 0.002165
+Epoch [3/5], Loss: 0.002075
+Epoch [4/5], Loss: 0.002055
+Epoch [5/5], Loss: 0.001954
+
+--- Training Stage 3/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.002046
+Epoch [2/5], Loss: 0.001985
+Epoch [3/5], Loss: 0.001922
+Epoch [4/5], Loss: 0.001922
+Epoch [5/5], Loss: 0.001881
+
+--- Training Stage 4/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.002132
+Epoch [2/5], Loss: 0.001979
+Epoch [3/5], Loss: 0.001911
+Epoch [4/5], Loss: 0.001874
+Epoch [5/5], Loss: 0.001861
+
+--- Training Stage 5/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.002138
+Epoch [2/5], Loss: 0.001955
+Epoch [3/5], Loss: 0.001915
+Epoch [4/5], Loss: 0.001896
+Epoch [5/5], Loss: 0.001876
+
+[+] Training complete for sigma=25/255, gamma=1.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=1.0, tau_init=0.05: 26.82 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma25_gamma1_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..eb70c3b1b72d24e28ffb8d326354c6f38f53d683
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma1p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage No-wave Model for sigma=25/255, gamma=1.0, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.008156
+Epoch [2/5], Loss: 0.004110
+Epoch [3/5], Loss: 0.002744
+Epoch [4/5], Loss: 0.002343
+Epoch [5/5], Loss: 0.002213
+
+--- Training Stage 2/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.003173
+Epoch [2/5], Loss: 0.002577
+Epoch [3/5], Loss: 0.002316
+Epoch [4/5], Loss: 0.002178
+Epoch [5/5], Loss: 0.002089
+
+--- Training Stage 3/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.002605
+Epoch [2/5], Loss: 0.002225
+Epoch [3/5], Loss: 0.002074
+Epoch [4/5], Loss: 0.002018
+Epoch [5/5], Loss: 0.001982
+
+--- Training Stage 4/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.002415
+Epoch [2/5], Loss: 0.002123
+Epoch [3/5], Loss: 0.002018
+Epoch [4/5], Loss: 0.001993
+Epoch [5/5], Loss: 0.001930
+
+--- Training Stage 5/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.002348
+Epoch [2/5], Loss: 0.002029
+Epoch [3/5], Loss: 0.001908
+Epoch [4/5], Loss: 0.001847
+Epoch [5/5], Loss: 0.001823
+
+[+] Training complete for sigma=25/255, gamma=1.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=1.0, tau_init=0.1: 26.86 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma25_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma1p0_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma1p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..84cf85e0f98197176cd9ba811e6819de0e8a98fa
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma1p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage No-wave Model for sigma=25/255, gamma=1.0, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.009184
+Epoch [2/5], Loss: 0.004348
+Epoch [3/5], Loss: 0.003041
+Epoch [4/5], Loss: 0.002511
+Epoch [5/5], Loss: 0.002236
+
+--- Training Stage 2/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.005451
+Epoch [2/5], Loss: 0.003379
+Epoch [3/5], Loss: 0.002652
+Epoch [4/5], Loss: 0.002326
+Epoch [5/5], Loss: 0.002180
+
+--- Training Stage 3/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.003120
+Epoch [2/5], Loss: 0.002238
+Epoch [3/5], Loss: 0.002035
+Epoch [4/5], Loss: 0.001982
+Epoch [5/5], Loss: 0.001928
+
+--- Training Stage 4/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.003027
+Epoch [2/5], Loss: 0.002127
+Epoch [3/5], Loss: 0.001926
+Epoch [4/5], Loss: 0.001859
+Epoch [5/5], Loss: 0.001814
+
+--- Training Stage 5/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.007406
+Epoch [2/5], Loss: 0.004162
+Epoch [3/5], Loss: 0.002960
+Epoch [4/5], Loss: 0.002425
+Epoch [5/5], Loss: 0.002179
+
+[+] Training complete for sigma=25/255, gamma=1.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=1.0, tau_init=0.2: 26.66 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma25_gamma1_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma2p0_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma2p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..290e3bbc1016049d5050e4f2eb2efa7d2b7327de
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma2p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage No-wave Model for sigma=25/255, gamma=2.0, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.008009
+Epoch [2/5], Loss: 0.005028
+Epoch [3/5], Loss: 0.003001
+Epoch [4/5], Loss: 0.002410
+Epoch [5/5], Loss: 0.002249
+
+--- Training Stage 2/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.002491
+Epoch [2/5], Loss: 0.002224
+Epoch [3/5], Loss: 0.002103
+Epoch [4/5], Loss: 0.002001
+Epoch [5/5], Loss: 0.001937
+
+--- Training Stage 3/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.002018
+Epoch [2/5], Loss: 0.001932
+Epoch [3/5], Loss: 0.001844
+Epoch [4/5], Loss: 0.001839
+Epoch [5/5], Loss: 0.001814
+
+--- Training Stage 4/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001895
+Epoch [2/5], Loss: 0.001818
+Epoch [3/5], Loss: 0.001797
+Epoch [4/5], Loss: 0.001788
+Epoch [5/5], Loss: 0.001808
+
+--- Training Stage 5/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.001851
+Epoch [2/5], Loss: 0.001785
+Epoch [3/5], Loss: 0.001779
+Epoch [4/5], Loss: 0.001745
+Epoch [5/5], Loss: 0.001752
+
+[+] Training complete for sigma=25/255, gamma=2.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=2.0, tau_init=0.05: 27.08 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma25_gamma2_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma2p0_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma2p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..21a203937e206f1c44c50cfe70d07e3705520799
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma2p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage No-wave Model for sigma=25/255, gamma=2.0, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.007965
+Epoch [2/5], Loss: 0.003996
+Epoch [3/5], Loss: 0.002710
+Epoch [4/5], Loss: 0.002387
+Epoch [5/5], Loss: 0.002177
+
+--- Training Stage 2/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.002613
+Epoch [2/5], Loss: 0.002168
+Epoch [3/5], Loss: 0.002037
+Epoch [4/5], Loss: 0.001938
+Epoch [5/5], Loss: 0.001857
+
+--- Training Stage 3/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.003116
+Epoch [2/5], Loss: 0.002218
+Epoch [3/5], Loss: 0.001932
+Epoch [4/5], Loss: 0.001887
+Epoch [5/5], Loss: 0.001844
+
+--- Training Stage 4/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.002259
+Epoch [2/5], Loss: 0.001876
+Epoch [3/5], Loss: 0.001779
+Epoch [4/5], Loss: 0.001736
+Epoch [5/5], Loss: 0.001722
+
+--- Training Stage 5/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.001958
+Epoch [2/5], Loss: 0.001763
+Epoch [3/5], Loss: 0.001740
+Epoch [4/5], Loss: 0.001718
+Epoch [5/5], Loss: 0.001688
+
+[+] Training complete for sigma=25/255, gamma=2.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=2.0, tau_init=0.1: 27.18 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma25_gamma2_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma2p0_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma2p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..c1f7df420537d78c88e079abeb88163f665630e5
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma25_gamma2p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage No-wave Model for sigma=25/255, gamma=2.0, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.018748
+Epoch [2/5], Loss: 0.007613
+Epoch [3/5], Loss: 0.004377
+Epoch [4/5], Loss: 0.003272
+Epoch [5/5], Loss: 0.002721
+
+--- Training Stage 2/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.005701
+Epoch [2/5], Loss: 0.003733
+Epoch [3/5], Loss: 0.002781
+Epoch [4/5], Loss: 0.002338
+Epoch [5/5], Loss: 0.002094
+
+--- Training Stage 3/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.003712
+Epoch [2/5], Loss: 0.002299
+Epoch [3/5], Loss: 0.002018
+Epoch [4/5], Loss: 0.001962
+Epoch [5/5], Loss: 0.001896
+
+--- Training Stage 4/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.002903
+Epoch [2/5], Loss: 0.002128
+Epoch [3/5], Loss: 0.001990
+Epoch [4/5], Loss: 0.001894
+Epoch [5/5], Loss: 0.001852
+
+--- Training Stage 5/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.003407
+Epoch [2/5], Loss: 0.002294
+Epoch [3/5], Loss: 0.002023
+Epoch [4/5], Loss: 0.001939
+Epoch [5/5], Loss: 0.001899
+
+[+] Training complete for sigma=25/255, gamma=2.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=2.0, tau_init=0.2: 26.93 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma25_gamma2_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..4981e3ab284b14cdcf656d9e5fba31cb45affbf7
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma0p5_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage No-wave Model for sigma=50/255, gamma=0.5, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.023578
+Epoch [2/5], Loss: 0.011729
+Epoch [3/5], Loss: 0.006010
+Epoch [4/5], Loss: 0.004971
+Epoch [5/5], Loss: 0.004658
+
+--- Training Stage 2/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.004627
+Epoch [2/5], Loss: 0.004374
+Epoch [3/5], Loss: 0.004311
+Epoch [4/5], Loss: 0.004237
+Epoch [5/5], Loss: 0.004124
+
+--- Training Stage 3/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.004233
+Epoch [2/5], Loss: 0.004135
+Epoch [3/5], Loss: 0.004061
+Epoch [4/5], Loss: 0.004081
+Epoch [5/5], Loss: 0.004024
+
+--- Training Stage 4/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.004086
+Epoch [2/5], Loss: 0.004064
+Epoch [3/5], Loss: 0.004015
+Epoch [4/5], Loss: 0.003947
+Epoch [5/5], Loss: 0.003961
+
+--- Training Stage 5/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.004066
+Epoch [2/5], Loss: 0.003947
+Epoch [3/5], Loss: 0.003974
+Epoch [4/5], Loss: 0.003876
+Epoch [5/5], Loss: 0.003863
+
+[+] Training complete for sigma=50/255, gamma=0.5, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=0.5, tau_init=0.05: 23.86 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma50_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..04f0fc274720dc7492e560c77b4f7bbadee0b7b7
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma0p5_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage No-wave Model for sigma=50/255, gamma=0.5, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.025927
+Epoch [2/5], Loss: 0.011812
+Epoch [3/5], Loss: 0.006651
+Epoch [4/5], Loss: 0.005591
+Epoch [5/5], Loss: 0.005111
+
+--- Training Stage 2/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.005149
+Epoch [2/5], Loss: 0.004552
+Epoch [3/5], Loss: 0.004370
+Epoch [4/5], Loss: 0.004262
+Epoch [5/5], Loss: 0.004213
+
+--- Training Stage 3/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.004953
+Epoch [2/5], Loss: 0.004403
+Epoch [3/5], Loss: 0.004179
+Epoch [4/5], Loss: 0.004134
+Epoch [5/5], Loss: 0.004065
+
+--- Training Stage 4/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.005937
+Epoch [2/5], Loss: 0.004843
+Epoch [3/5], Loss: 0.004446
+Epoch [4/5], Loss: 0.004284
+Epoch [5/5], Loss: 0.004191
+
+--- Training Stage 5/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.005522
+Epoch [2/5], Loss: 0.004727
+Epoch [3/5], Loss: 0.004421
+Epoch [4/5], Loss: 0.004135
+Epoch [5/5], Loss: 0.003974
+
+[+] Training complete for sigma=50/255, gamma=0.5, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=0.5, tau_init=0.1: 23.84 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma50_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..4fe7e2d32b0b52bbe2a6afd88c3f661472800f0c
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma0p5_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage No-wave Model for sigma=50/255, gamma=0.5, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.036220
+Epoch [2/5], Loss: 0.013974
+Epoch [3/5], Loss: 0.007450
+Epoch [4/5], Loss: 0.005613
+Epoch [5/5], Loss: 0.005026
+
+--- Training Stage 2/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.006414
+Epoch [2/5], Loss: 0.005022
+Epoch [3/5], Loss: 0.004544
+Epoch [4/5], Loss: 0.004353
+Epoch [5/5], Loss: 0.004319
+
+--- Training Stage 3/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.005616
+Epoch [2/5], Loss: 0.004598
+Epoch [3/5], Loss: 0.004327
+Epoch [4/5], Loss: 0.004255
+Epoch [5/5], Loss: 0.004138
+
+--- Training Stage 4/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.008435
+Epoch [2/5], Loss: 0.005273
+Epoch [3/5], Loss: 0.004297
+Epoch [4/5], Loss: 0.004121
+Epoch [5/5], Loss: 0.004030
+
+--- Training Stage 5/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.006813
+Epoch [2/5], Loss: 0.005106
+Epoch [3/5], Loss: 0.004425
+Epoch [4/5], Loss: 0.004134
+Epoch [5/5], Loss: 0.004057
+
+[+] Training complete for sigma=50/255, gamma=0.5, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=0.5, tau_init=0.2: 23.83 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma50_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..ed59ddd5b786aa8bdd369b18c073dee41f5f44a4
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage No-wave Model for sigma=50/255, gamma=1.0, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.021513
+Epoch [2/5], Loss: 0.010911
+Epoch [3/5], Loss: 0.006024
+Epoch [4/5], Loss: 0.004990
+Epoch [5/5], Loss: 0.004738
+
+--- Training Stage 2/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.004719
+Epoch [2/5], Loss: 0.004533
+Epoch [3/5], Loss: 0.004388
+Epoch [4/5], Loss: 0.004275
+Epoch [5/5], Loss: 0.004269
+
+--- Training Stage 3/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.004404
+Epoch [2/5], Loss: 0.004325
+Epoch [3/5], Loss: 0.004249
+Epoch [4/5], Loss: 0.004157
+Epoch [5/5], Loss: 0.004109
+
+--- Training Stage 4/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.004254
+Epoch [2/5], Loss: 0.004175
+Epoch [3/5], Loss: 0.004099
+Epoch [4/5], Loss: 0.004098
+Epoch [5/5], Loss: 0.004080
+
+--- Training Stage 5/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.004147
+Epoch [2/5], Loss: 0.004093
+Epoch [3/5], Loss: 0.004008
+Epoch [4/5], Loss: 0.004008
+Epoch [5/5], Loss: 0.003946
+
+[+] Training complete for sigma=50/255, gamma=1.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=1.0, tau_init=0.05: 23.82 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma50_gamma1_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..75a21c57fa27f49989c0a4a0a8103ff19700f302
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma1p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage No-wave Model for sigma=50/255, gamma=1.0, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.027186
+Epoch [2/5], Loss: 0.012674
+Epoch [3/5], Loss: 0.006804
+Epoch [4/5], Loss: 0.005630
+Epoch [5/5], Loss: 0.004997
+
+--- Training Stage 2/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.005383
+Epoch [2/5], Loss: 0.004662
+Epoch [3/5], Loss: 0.004406
+Epoch [4/5], Loss: 0.004356
+Epoch [5/5], Loss: 0.004263
+
+--- Training Stage 3/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.004678
+Epoch [2/5], Loss: 0.004215
+Epoch [3/5], Loss: 0.004121
+Epoch [4/5], Loss: 0.004109
+Epoch [5/5], Loss: 0.004026
+
+--- Training Stage 4/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.004983
+Epoch [2/5], Loss: 0.004380
+Epoch [3/5], Loss: 0.004150
+Epoch [4/5], Loss: 0.004119
+Epoch [5/5], Loss: 0.004060
+
+--- Training Stage 5/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.004334
+Epoch [2/5], Loss: 0.004092
+Epoch [3/5], Loss: 0.003943
+Epoch [4/5], Loss: 0.003955
+Epoch [5/5], Loss: 0.003955
+
+[+] Training complete for sigma=50/255, gamma=1.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=1.0, tau_init=0.1: 23.79 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma50_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma1p0_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma1p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..603289f4aab9e62fd758acbc640a9011e3b8fa12
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma1p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage No-wave Model for sigma=50/255, gamma=1.0, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.035047
+Epoch [2/5], Loss: 0.011431
+Epoch [3/5], Loss: 0.007305
+Epoch [4/5], Loss: 0.005926
+Epoch [5/5], Loss: 0.005273
+
+--- Training Stage 2/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.007744
+Epoch [2/5], Loss: 0.005471
+Epoch [3/5], Loss: 0.004847
+Epoch [4/5], Loss: 0.004557
+Epoch [5/5], Loss: 0.004492
+
+--- Training Stage 3/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.007479
+Epoch [2/5], Loss: 0.005525
+Epoch [3/5], Loss: 0.004884
+Epoch [4/5], Loss: 0.004512
+Epoch [5/5], Loss: 0.004317
+
+--- Training Stage 4/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.006252
+Epoch [2/5], Loss: 0.004803
+Epoch [3/5], Loss: 0.004344
+Epoch [4/5], Loss: 0.004219
+Epoch [5/5], Loss: 0.004184
+
+--- Training Stage 5/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.007255
+Epoch [2/5], Loss: 0.005251
+Epoch [3/5], Loss: 0.004622
+Epoch [4/5], Loss: 0.004325
+Epoch [5/5], Loss: 0.004153
+
+[+] Training complete for sigma=50/255, gamma=1.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=1.0, tau_init=0.2: 23.71 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma50_gamma1_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma2p0_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma2p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..a6eaf714d7e69a7da86f02e713f44a0fb34aef89
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma2p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage No-wave Model for sigma=50/255, gamma=2.0, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.023518
+Epoch [2/5], Loss: 0.012282
+Epoch [3/5], Loss: 0.006813
+Epoch [4/5], Loss: 0.005650
+Epoch [5/5], Loss: 0.005248
+
+--- Training Stage 2/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.005353
+Epoch [2/5], Loss: 0.005053
+Epoch [3/5], Loss: 0.004821
+Epoch [4/5], Loss: 0.004570
+Epoch [5/5], Loss: 0.004381
+
+--- Training Stage 3/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.004627
+Epoch [2/5], Loss: 0.004357
+Epoch [3/5], Loss: 0.004324
+Epoch [4/5], Loss: 0.004208
+Epoch [5/5], Loss: 0.004130
+
+--- Training Stage 4/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.004170
+Epoch [2/5], Loss: 0.004145
+Epoch [3/5], Loss: 0.004128
+Epoch [4/5], Loss: 0.004112
+Epoch [5/5], Loss: 0.004054
+
+--- Training Stage 5/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.004235
+Epoch [2/5], Loss: 0.004142
+Epoch [3/5], Loss: 0.004140
+Epoch [4/5], Loss: 0.004062
+Epoch [5/5], Loss: 0.004097
+
+[+] Training complete for sigma=50/255, gamma=2.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=2.0, tau_init=0.05: 23.71 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma50_gamma2_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma2p0_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma2p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..96ba0a566f2b0a056d84d76457ab9848792592e1
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma2p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage No-wave Model for sigma=50/255, gamma=2.0, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.021039
+Epoch [2/5], Loss: 0.009256
+Epoch [3/5], Loss: 0.006013
+Epoch [4/5], Loss: 0.005251
+Epoch [5/5], Loss: 0.004859
+
+--- Training Stage 2/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.005678
+Epoch [2/5], Loss: 0.004923
+Epoch [3/5], Loss: 0.004656
+Epoch [4/5], Loss: 0.004510
+Epoch [5/5], Loss: 0.004334
+
+--- Training Stage 3/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.004762
+Epoch [2/5], Loss: 0.004278
+Epoch [3/5], Loss: 0.004205
+Epoch [4/5], Loss: 0.004196
+Epoch [5/5], Loss: 0.004139
+
+--- Training Stage 4/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.005124
+Epoch [2/5], Loss: 0.004484
+Epoch [3/5], Loss: 0.004292
+Epoch [4/5], Loss: 0.004228
+Epoch [5/5], Loss: 0.004192
+
+--- Training Stage 5/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.005796
+Epoch [2/5], Loss: 0.004771
+Epoch [3/5], Loss: 0.004447
+Epoch [4/5], Loss: 0.004361
+Epoch [5/5], Loss: 0.004229
+
+[+] Training complete for sigma=50/255, gamma=2.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=2.0, tau_init=0.1: 23.58 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma50_gamma2_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma2p0_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma2p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..ce8813375e390c24c54dff5dc2c7c2e4d2dc3a2f
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma50_gamma2p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage No-wave Model for sigma=50/255, gamma=2.0, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.026159
+Epoch [2/5], Loss: 0.010541
+Epoch [3/5], Loss: 0.007089
+Epoch [4/5], Loss: 0.005865
+Epoch [5/5], Loss: 0.005386
+
+--- Training Stage 2/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.006455
+Epoch [2/5], Loss: 0.004961
+Epoch [3/5], Loss: 0.004583
+Epoch [4/5], Loss: 0.004435
+Epoch [5/5], Loss: 0.004397
+
+--- Training Stage 3/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.008237
+Epoch [2/5], Loss: 0.005707
+Epoch [3/5], Loss: 0.004878
+Epoch [4/5], Loss: 0.004648
+Epoch [5/5], Loss: 0.004425
+
+--- Training Stage 4/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.005087
+Epoch [2/5], Loss: 0.004509
+Epoch [3/5], Loss: 0.004314
+Epoch [4/5], Loss: 0.004187
+Epoch [5/5], Loss: 0.004197
+
+--- Training Stage 5/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.008975
+Epoch [2/5], Loss: 0.006156
+Epoch [3/5], Loss: 0.005138
+Epoch [4/5], Loss: 0.004619
+Epoch [5/5], Loss: 0.004343
+
+[+] Training complete for sigma=50/255, gamma=2.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=2.0, tau_init=0.2: 23.65 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma50_gamma2_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..c4afcd4e7d578d0e1bb0180fedc6daacbd1f7141
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma0p5_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage No-wave Model for sigma=75/255, gamma=0.5, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.046871
+Epoch [2/5], Loss: 0.025825
+Epoch [3/5], Loss: 0.013078
+Epoch [4/5], Loss: 0.009530
+Epoch [5/5], Loss: 0.008405
+
+--- Training Stage 2/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.008070
+Epoch [2/5], Loss: 0.007623
+Epoch [3/5], Loss: 0.007259
+Epoch [4/5], Loss: 0.007190
+Epoch [5/5], Loss: 0.007127
+
+--- Training Stage 3/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.007261
+Epoch [2/5], Loss: 0.007104
+Epoch [3/5], Loss: 0.007110
+Epoch [4/5], Loss: 0.007075
+Epoch [5/5], Loss: 0.007008
+
+--- Training Stage 4/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.007041
+Epoch [2/5], Loss: 0.006959
+Epoch [3/5], Loss: 0.006919
+Epoch [4/5], Loss: 0.006777
+Epoch [5/5], Loss: 0.006783
+
+--- Training Stage 5/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.006867
+Epoch [2/5], Loss: 0.006820
+Epoch [3/5], Loss: 0.006788
+Epoch [4/5], Loss: 0.006701
+Epoch [5/5], Loss: 0.006678
+
+[+] Training complete for sigma=75/255, gamma=0.5, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=0.5, tau_init=0.05: 21.82 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma75_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..040de4e6bae141dfe480c720351e3c42bfb3f6dc
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma0p5_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage No-wave Model for sigma=75/255, gamma=0.5, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.038489
+Epoch [2/5], Loss: 0.013925
+Epoch [3/5], Loss: 0.009777
+Epoch [4/5], Loss: 0.008564
+Epoch [5/5], Loss: 0.008144
+
+--- Training Stage 2/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.007931
+Epoch [2/5], Loss: 0.007593
+Epoch [3/5], Loss: 0.007327
+Epoch [4/5], Loss: 0.007219
+Epoch [5/5], Loss: 0.007185
+
+--- Training Stage 3/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.007701
+Epoch [2/5], Loss: 0.007197
+Epoch [3/5], Loss: 0.007151
+Epoch [4/5], Loss: 0.007086
+Epoch [5/5], Loss: 0.007076
+
+--- Training Stage 4/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.008127
+Epoch [2/5], Loss: 0.007442
+Epoch [3/5], Loss: 0.007150
+Epoch [4/5], Loss: 0.007054
+Epoch [5/5], Loss: 0.006983
+
+--- Training Stage 5/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.007318
+Epoch [2/5], Loss: 0.007031
+Epoch [3/5], Loss: 0.006931
+Epoch [4/5], Loss: 0.006883
+Epoch [5/5], Loss: 0.006836
+
+[+] Training complete for sigma=75/255, gamma=0.5, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=0.5, tau_init=0.1: 21.71 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma75_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..83cee5687a54ec35addb81684612d0536c154424
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma0p5_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage No-wave Model for sigma=75/255, gamma=0.5, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.029260
+Epoch [2/5], Loss: 0.011246
+Epoch [3/5], Loss: 0.008150
+Epoch [4/5], Loss: 0.007582
+Epoch [5/5], Loss: 0.007419
+
+--- Training Stage 2/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.009369
+Epoch [2/5], Loss: 0.007819
+Epoch [3/5], Loss: 0.007385
+Epoch [4/5], Loss: 0.007218
+Epoch [5/5], Loss: 0.007197
+
+--- Training Stage 3/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.009228
+Epoch [2/5], Loss: 0.007737
+Epoch [3/5], Loss: 0.007186
+Epoch [4/5], Loss: 0.007066
+Epoch [5/5], Loss: 0.007014
+
+--- Training Stage 4/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.008479
+Epoch [2/5], Loss: 0.007493
+Epoch [3/5], Loss: 0.007143
+Epoch [4/5], Loss: 0.007061
+Epoch [5/5], Loss: 0.006985
+
+--- Training Stage 5/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.008280
+Epoch [2/5], Loss: 0.007361
+Epoch [3/5], Loss: 0.007090
+Epoch [4/5], Loss: 0.006866
+Epoch [5/5], Loss: 0.006809
+
+[+] Training complete for sigma=75/255, gamma=0.5, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=0.5, tau_init=0.2: 21.75 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma75_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..2eb177a5fc12ef0af5fea9b74d4e65b0975b2177
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage No-wave Model for sigma=75/255, gamma=1.0, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.050002
+Epoch [2/5], Loss: 0.026323
+Epoch [3/5], Loss: 0.013475
+Epoch [4/5], Loss: 0.010244
+Epoch [5/5], Loss: 0.008904
+
+--- Training Stage 2/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.008351
+Epoch [2/5], Loss: 0.007760
+Epoch [3/5], Loss: 0.007332
+Epoch [4/5], Loss: 0.007216
+Epoch [5/5], Loss: 0.007123
+
+--- Training Stage 3/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.007314
+Epoch [2/5], Loss: 0.007127
+Epoch [3/5], Loss: 0.007066
+Epoch [4/5], Loss: 0.006936
+Epoch [5/5], Loss: 0.006989
+
+--- Training Stage 4/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.007130
+Epoch [2/5], Loss: 0.006986
+Epoch [3/5], Loss: 0.006917
+Epoch [4/5], Loss: 0.006946
+Epoch [5/5], Loss: 0.006850
+
+--- Training Stage 5/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.006954
+Epoch [2/5], Loss: 0.006830
+Epoch [3/5], Loss: 0.006767
+Epoch [4/5], Loss: 0.006728
+Epoch [5/5], Loss: 0.006618
+
+[+] Training complete for sigma=75/255, gamma=1.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=1.0, tau_init=0.05: 21.86 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma75_gamma1_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..75b35929ef46fc825efe596b1bf70f0dffe34c34
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma1p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage No-wave Model for sigma=75/255, gamma=1.0, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.047866
+Epoch [2/5], Loss: 0.021543
+Epoch [3/5], Loss: 0.011643
+Epoch [4/5], Loss: 0.009217
+Epoch [5/5], Loss: 0.008208
+
+--- Training Stage 2/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.007842
+Epoch [2/5], Loss: 0.007191
+Epoch [3/5], Loss: 0.007069
+Epoch [4/5], Loss: 0.006998
+Epoch [5/5], Loss: 0.006923
+
+--- Training Stage 3/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.007653
+Epoch [2/5], Loss: 0.007103
+Epoch [3/5], Loss: 0.006904
+Epoch [4/5], Loss: 0.006865
+Epoch [5/5], Loss: 0.006885
+
+--- Training Stage 4/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.006941
+Epoch [2/5], Loss: 0.006774
+Epoch [3/5], Loss: 0.006771
+Epoch [4/5], Loss: 0.006706
+Epoch [5/5], Loss: 0.006685
+
+--- Training Stage 5/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.007269
+Epoch [2/5], Loss: 0.006908
+Epoch [3/5], Loss: 0.006791
+Epoch [4/5], Loss: 0.006716
+Epoch [5/5], Loss: 0.006675
+
+[+] Training complete for sigma=75/255, gamma=1.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=1.0, tau_init=0.1: 21.85 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma75_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma1p0_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma1p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..3b746391a2768ea193f5a26545676a0faf9b72c6
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma1p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage No-wave Model for sigma=75/255, gamma=1.0, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.034541
+Epoch [2/5], Loss: 0.015685
+Epoch [3/5], Loss: 0.010460
+Epoch [4/5], Loss: 0.008623
+Epoch [5/5], Loss: 0.008196
+
+--- Training Stage 2/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.008743
+Epoch [2/5], Loss: 0.007565
+Epoch [3/5], Loss: 0.007186
+Epoch [4/5], Loss: 0.007104
+Epoch [5/5], Loss: 0.007095
+
+--- Training Stage 3/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.010366
+Epoch [2/5], Loss: 0.008448
+Epoch [3/5], Loss: 0.007618
+Epoch [4/5], Loss: 0.007338
+Epoch [5/5], Loss: 0.007100
+
+--- Training Stage 4/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.008273
+Epoch [2/5], Loss: 0.007371
+Epoch [3/5], Loss: 0.006991
+Epoch [4/5], Loss: 0.006794
+Epoch [5/5], Loss: 0.006843
+
+--- Training Stage 5/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.009442
+Epoch [2/5], Loss: 0.007649
+Epoch [3/5], Loss: 0.006933
+Epoch [4/5], Loss: 0.006656
+Epoch [5/5], Loss: 0.006675
+
+[+] Training complete for sigma=75/255, gamma=1.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=1.0, tau_init=0.2: 21.84 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma75_gamma1_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma2p0_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma2p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..287a0adfbd54ed44672a9cb27bbc49b7c646a379
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma2p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage No-wave Model for sigma=75/255, gamma=2.0, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.040910
+Epoch [2/5], Loss: 0.020750
+Epoch [3/5], Loss: 0.011048
+Epoch [4/5], Loss: 0.009215
+Epoch [5/5], Loss: 0.008772
+
+--- Training Stage 2/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.008189
+Epoch [2/5], Loss: 0.007730
+Epoch [3/5], Loss: 0.007227
+Epoch [4/5], Loss: 0.006956
+Epoch [5/5], Loss: 0.006893
+
+--- Training Stage 3/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.007024
+Epoch [2/5], Loss: 0.006827
+Epoch [3/5], Loss: 0.006817
+Epoch [4/5], Loss: 0.006826
+Epoch [5/5], Loss: 0.006782
+
+--- Training Stage 4/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.006762
+Epoch [2/5], Loss: 0.006741
+Epoch [3/5], Loss: 0.006723
+Epoch [4/5], Loss: 0.006639
+Epoch [5/5], Loss: 0.006690
+
+--- Training Stage 5/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.006714
+Epoch [2/5], Loss: 0.006609
+Epoch [3/5], Loss: 0.006627
+Epoch [4/5], Loss: 0.006586
+Epoch [5/5], Loss: 0.006592
+
+[+] Training complete for sigma=75/255, gamma=2.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=2.0, tau_init=0.05: 21.89 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma75_gamma2_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma2p0_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma2p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..5788d1f1c4ae7dd7c01b54ae671c25f51a9556e9
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma2p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage No-wave Model for sigma=75/255, gamma=2.0, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.047535
+Epoch [2/5], Loss: 0.019950
+Epoch [3/5], Loss: 0.012030
+Epoch [4/5], Loss: 0.010302
+Epoch [5/5], Loss: 0.009573
+
+--- Training Stage 2/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.009547
+Epoch [2/5], Loss: 0.008036
+Epoch [3/5], Loss: 0.007541
+Epoch [4/5], Loss: 0.007346
+Epoch [5/5], Loss: 0.007304
+
+--- Training Stage 3/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.009086
+Epoch [2/5], Loss: 0.008024
+Epoch [3/5], Loss: 0.007515
+Epoch [4/5], Loss: 0.007280
+Epoch [5/5], Loss: 0.007140
+
+--- Training Stage 4/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.007298
+Epoch [2/5], Loss: 0.007153
+Epoch [3/5], Loss: 0.007082
+Epoch [4/5], Loss: 0.007009
+Epoch [5/5], Loss: 0.006926
+
+--- Training Stage 5/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.007222
+Epoch [2/5], Loss: 0.006971
+Epoch [3/5], Loss: 0.006922
+Epoch [4/5], Loss: 0.006950
+Epoch [5/5], Loss: 0.006942
+
+[+] Training complete for sigma=75/255, gamma=2.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=2.0, tau_init=0.1: 21.69 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma75_gamma2_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma2p0_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma2p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..c0f36661d26eca2dfe2ff1770cca65cd66cc3d69
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_nowave_sigma75_gamma2p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage No-wave Model for sigma=75/255, gamma=2.0, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.085263
+Epoch [2/5], Loss: 0.036507
+Epoch [3/5], Loss: 0.019922
+Epoch [4/5], Loss: 0.013241
+Epoch [5/5], Loss: 0.011414
+
+--- Training Stage 2/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.013282
+Epoch [2/5], Loss: 0.009454
+Epoch [3/5], Loss: 0.008300
+Epoch [4/5], Loss: 0.007745
+Epoch [5/5], Loss: 0.007526
+
+--- Training Stage 3/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.009820
+Epoch [2/5], Loss: 0.008091
+Epoch [3/5], Loss: 0.007634
+Epoch [4/5], Loss: 0.007354
+Epoch [5/5], Loss: 0.007239
+
+--- Training Stage 4/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.008128
+Epoch [2/5], Loss: 0.007187
+Epoch [3/5], Loss: 0.007058
+Epoch [4/5], Loss: 0.006871
+Epoch [5/5], Loss: 0.006951
+
+--- Training Stage 5/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.007629
+Epoch [2/5], Loss: 0.007024
+Epoch [3/5], Loss: 0.006937
+Epoch [4/5], Loss: 0.006770
+Epoch [5/5], Loss: 0.006715
+
+[+] Training complete for sigma=75/255, gamma=2.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=2.0, tau_init=0.2: 21.91 dB
+[+] Saved checkpoint: model_5stages_waveFalse_sigma75_gamma2_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..fc2701527a792a8666dda102738078386c9d9880
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma0p5_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph Model for sigma=15/255, gamma=0.5, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.003251
+Epoch [2/5], Loss: 0.003075
+Epoch [3/5], Loss: 0.002655
+Epoch [4/5], Loss: 0.001901
+Epoch [5/5], Loss: 0.001312
+
+--- Training Stage 2/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.002774
+Epoch [2/5], Loss: 0.002755
+Epoch [3/5], Loss: 0.002562
+Epoch [4/5], Loss: 0.002253
+Epoch [5/5], Loss: 0.001866
+
+--- Training Stage 3/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.003776
+Epoch [2/5], Loss: 0.003706
+Epoch [3/5], Loss: 0.003495
+Epoch [4/5], Loss: 0.003109
+Epoch [5/5], Loss: 0.002450
+
+--- Training Stage 4/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.003293
+Epoch [2/5], Loss: 0.003207
+Epoch [3/5], Loss: 0.003079
+Epoch [4/5], Loss: 0.002871
+Epoch [5/5], Loss: 0.002489
+
+--- Training Stage 5/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.003872
+Epoch [2/5], Loss: 0.003846
+Epoch [3/5], Loss: 0.003781
+Epoch [4/5], Loss: 0.003580
+Epoch [5/5], Loss: 0.003165
+
+[+] Training complete for sigma=15/255, gamma=0.5, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=0.5, tau_init=0.05: 25.34 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma15_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..8670257f9d820139121f957a977ad6376623e61a
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma0p5_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph Model for sigma=15/255, gamma=0.5, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.003250
+Epoch [2/5], Loss: 0.002797
+Epoch [3/5], Loss: 0.001972
+Epoch [4/5], Loss: 0.001394
+Epoch [5/5], Loss: 0.001248
+
+--- Training Stage 2/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.002905
+Epoch [2/5], Loss: 0.002740
+Epoch [3/5], Loss: 0.002426
+Epoch [4/5], Loss: 0.002114
+Epoch [5/5], Loss: 0.001794
+
+--- Training Stage 3/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.003289
+Epoch [2/5], Loss: 0.003042
+Epoch [3/5], Loss: 0.002473
+Epoch [4/5], Loss: 0.001765
+Epoch [5/5], Loss: 0.001399
+
+--- Training Stage 4/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.002199
+Epoch [2/5], Loss: 0.002161
+Epoch [3/5], Loss: 0.002044
+Epoch [4/5], Loss: 0.001909
+Epoch [5/5], Loss: 0.001818
+
+--- Training Stage 5/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.003380
+Epoch [2/5], Loss: 0.003222
+Epoch [3/5], Loss: 0.002722
+Epoch [4/5], Loss: 0.002126
+Epoch [5/5], Loss: 0.001808
+
+[+] Training complete for sigma=15/255, gamma=0.5, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=0.5, tau_init=0.1: 27.18 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma15_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..041e821a7ec5ab549111ba22fa53f74d53fddaf3
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma0p5_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph Model for sigma=15/255, gamma=0.5, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.003061
+Epoch [2/5], Loss: 0.002219
+Epoch [3/5], Loss: 0.001474
+Epoch [4/5], Loss: 0.001213
+Epoch [5/5], Loss: 0.001121
+
+--- Training Stage 2/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.002874
+Epoch [2/5], Loss: 0.002473
+Epoch [3/5], Loss: 0.001958
+Epoch [4/5], Loss: 0.001663
+Epoch [5/5], Loss: 0.001514
+
+--- Training Stage 3/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.003232
+Epoch [2/5], Loss: 0.002836
+Epoch [3/5], Loss: 0.002386
+Epoch [4/5], Loss: 0.001827
+Epoch [5/5], Loss: 0.001439
+
+--- Training Stage 4/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.002339
+Epoch [2/5], Loss: 0.002235
+Epoch [3/5], Loss: 0.002139
+Epoch [4/5], Loss: 0.002080
+Epoch [5/5], Loss: 0.001982
+
+--- Training Stage 5/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.003290
+Epoch [2/5], Loss: 0.002795
+Epoch [3/5], Loss: 0.002238
+Epoch [4/5], Loss: 0.002016
+Epoch [5/5], Loss: 0.001932
+
+[+] Training complete for sigma=15/255, gamma=0.5, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=0.5, tau_init=0.2: 26.66 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma15_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..279bf2b7d4fd23da39ccf3d6e9cedc1495de8c62
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph Model for sigma=15/255, gamma=1.0, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.003282
+Epoch [2/5], Loss: 0.003135
+Epoch [3/5], Loss: 0.002714
+Epoch [4/5], Loss: 0.001908
+Epoch [5/5], Loss: 0.001285
+
+--- Training Stage 2/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.002650
+Epoch [2/5], Loss: 0.002599
+Epoch [3/5], Loss: 0.002425
+Epoch [4/5], Loss: 0.002121
+Epoch [5/5], Loss: 0.001761
+
+--- Training Stage 3/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.003445
+Epoch [2/5], Loss: 0.003376
+Epoch [3/5], Loss: 0.003221
+Epoch [4/5], Loss: 0.002868
+Epoch [5/5], Loss: 0.002277
+
+--- Training Stage 4/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.002699
+Epoch [2/5], Loss: 0.002646
+Epoch [3/5], Loss: 0.002515
+Epoch [4/5], Loss: 0.002280
+Epoch [5/5], Loss: 0.001950
+
+--- Training Stage 5/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.002775
+Epoch [2/5], Loss: 0.002763
+Epoch [3/5], Loss: 0.002659
+Epoch [4/5], Loss: 0.002612
+Epoch [5/5], Loss: 0.002410
+
+[+] Training complete for sigma=15/255, gamma=1.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=1.0, tau_init=0.05: 25.93 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma15_gamma1_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..70a66d28ecf5be772a3d67ef4320b7e823a85e25
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma1p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph Model for sigma=15/255, gamma=1.0, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.003257
+Epoch [2/5], Loss: 0.002869
+Epoch [3/5], Loss: 0.002152
+Epoch [4/5], Loss: 0.001471
+Epoch [5/5], Loss: 0.001192
+
+--- Training Stage 2/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.002522
+Epoch [2/5], Loss: 0.002358
+Epoch [3/5], Loss: 0.002049
+Epoch [4/5], Loss: 0.001684
+Epoch [5/5], Loss: 0.001397
+
+--- Training Stage 3/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.002367
+Epoch [2/5], Loss: 0.002242
+Epoch [3/5], Loss: 0.002006
+Epoch [4/5], Loss: 0.001651
+Epoch [5/5], Loss: 0.001295
+
+--- Training Stage 4/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.001718
+Epoch [2/5], Loss: 0.001701
+Epoch [3/5], Loss: 0.001590
+Epoch [4/5], Loss: 0.001557
+Epoch [5/5], Loss: 0.001472
+
+--- Training Stage 5/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.002359
+Epoch [2/5], Loss: 0.002251
+Epoch [3/5], Loss: 0.002074
+Epoch [4/5], Loss: 0.001778
+Epoch [5/5], Loss: 0.001547
+
+[+] Training complete for sigma=15/255, gamma=1.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=1.0, tau_init=0.1: 27.78 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma15_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma1p0_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma1p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..bc547b9c3dbff039093d43d8ffa0032684b127f3
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma1p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph Model for sigma=15/255, gamma=1.0, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.003045
+Epoch [2/5], Loss: 0.002321
+Epoch [3/5], Loss: 0.001724
+Epoch [4/5], Loss: 0.001405
+Epoch [5/5], Loss: 0.001251
+
+--- Training Stage 2/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.002490
+Epoch [2/5], Loss: 0.002139
+Epoch [3/5], Loss: 0.001891
+Epoch [4/5], Loss: 0.001770
+Epoch [5/5], Loss: 0.001682
+
+--- Training Stage 3/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.003061
+Epoch [2/5], Loss: 0.002553
+Epoch [3/5], Loss: 0.001923
+Epoch [4/5], Loss: 0.001549
+Epoch [5/5], Loss: 0.001360
+
+--- Training Stage 4/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.002086
+Epoch [2/5], Loss: 0.001958
+Epoch [3/5], Loss: 0.001806
+Epoch [4/5], Loss: 0.001652
+Epoch [5/5], Loss: 0.001567
+
+--- Training Stage 5/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.002329
+Epoch [2/5], Loss: 0.002074
+Epoch [3/5], Loss: 0.001705
+Epoch [4/5], Loss: 0.001554
+Epoch [5/5], Loss: 0.001558
+
+[+] Training complete for sigma=15/255, gamma=1.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=1.0, tau_init=0.2: 27.63 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma15_gamma1_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma2p0_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma2p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..447a39eb4e55a088042462f19014add95e506585
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma2p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph Model for sigma=15/255, gamma=2.0, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.003251
+Epoch [2/5], Loss: 0.003075
+Epoch [3/5], Loss: 0.002642
+Epoch [4/5], Loss: 0.001916
+Epoch [5/5], Loss: 0.001327
+
+--- Training Stage 2/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.002409
+Epoch [2/5], Loss: 0.002303
+Epoch [3/5], Loss: 0.002148
+Epoch [4/5], Loss: 0.001919
+Epoch [5/5], Loss: 0.001536
+
+--- Training Stage 3/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.002349
+Epoch [2/5], Loss: 0.002348
+Epoch [3/5], Loss: 0.002253
+Epoch [4/5], Loss: 0.002129
+Epoch [5/5], Loss: 0.001942
+
+--- Training Stage 4/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.002605
+Epoch [2/5], Loss: 0.002554
+Epoch [3/5], Loss: 0.002513
+Epoch [4/5], Loss: 0.002305
+Epoch [5/5], Loss: 0.002028
+
+--- Training Stage 5/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.002104
+Epoch [2/5], Loss: 0.002135
+Epoch [3/5], Loss: 0.002085
+Epoch [4/5], Loss: 0.002039
+Epoch [5/5], Loss: 0.001848
+
+[+] Training complete for sigma=15/255, gamma=2.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=2.0, tau_init=0.05: 27.06 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma15_gamma2_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma2p0_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma2p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..cadd3a7f22f811d1e18bc39e450dbf3285249239
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma2p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph Model for sigma=15/255, gamma=2.0, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.003112
+Epoch [2/5], Loss: 0.002608
+Epoch [3/5], Loss: 0.001802
+Epoch [4/5], Loss: 0.001277
+Epoch [5/5], Loss: 0.001161
+
+--- Training Stage 2/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.002578
+Epoch [2/5], Loss: 0.002409
+Epoch [3/5], Loss: 0.002095
+Epoch [4/5], Loss: 0.001796
+Epoch [5/5], Loss: 0.001577
+
+--- Training Stage 3/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.002652
+Epoch [2/5], Loss: 0.002503
+Epoch [3/5], Loss: 0.002212
+Epoch [4/5], Loss: 0.001820
+Epoch [5/5], Loss: 0.001446
+
+--- Training Stage 4/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.001572
+Epoch [2/5], Loss: 0.001535
+Epoch [3/5], Loss: 0.001520
+Epoch [4/5], Loss: 0.001424
+Epoch [5/5], Loss: 0.001350
+
+--- Training Stage 5/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.001775
+Epoch [2/5], Loss: 0.001715
+Epoch [3/5], Loss: 0.001615
+Epoch [4/5], Loss: 0.001512
+Epoch [5/5], Loss: 0.001383
+
+[+] Training complete for sigma=15/255, gamma=2.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=2.0, tau_init=0.1: 28.13 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma15_gamma2_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma2p0_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma2p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..ee8dabc3d7efdf18ce98e4a842f6b0480bf1dc5c
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma15_gamma2p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph Model for sigma=15/255, gamma=2.0, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.003076
+Epoch [2/5], Loss: 0.002230
+Epoch [3/5], Loss: 0.001600
+Epoch [4/5], Loss: 0.001348
+Epoch [5/5], Loss: 0.001202
+
+--- Training Stage 2/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.002118
+Epoch [2/5], Loss: 0.001864
+Epoch [3/5], Loss: 0.001631
+Epoch [4/5], Loss: 0.001440
+Epoch [5/5], Loss: 0.001325
+
+--- Training Stage 3/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.001905
+Epoch [2/5], Loss: 0.001654
+Epoch [3/5], Loss: 0.001378
+Epoch [4/5], Loss: 0.001194
+Epoch [5/5], Loss: 0.001100
+
+--- Training Stage 4/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.001424
+Epoch [2/5], Loss: 0.001380
+Epoch [3/5], Loss: 0.001315
+Epoch [4/5], Loss: 0.001242
+Epoch [5/5], Loss: 0.001191
+
+--- Training Stage 5/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.001499
+Epoch [2/5], Loss: 0.001421
+Epoch [3/5], Loss: 0.001313
+Epoch [4/5], Loss: 0.001240
+Epoch [5/5], Loss: 0.001197
+
+[+] Training complete for sigma=15/255, gamma=2.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=15/255, gamma=2.0, tau_init=0.2: 28.53 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma15_gamma2_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..952b94588aed8319454cb1111bd7caa9838979a1
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma0p5_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph Model for sigma=25/255, gamma=0.5, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.008710
+Epoch [2/5], Loss: 0.008187
+Epoch [3/5], Loss: 0.006801
+Epoch [4/5], Loss: 0.004468
+Epoch [5/5], Loss: 0.002700
+
+--- Training Stage 2/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.007327
+Epoch [2/5], Loss: 0.007157
+Epoch [3/5], Loss: 0.006882
+Epoch [4/5], Loss: 0.006244
+Epoch [5/5], Loss: 0.005345
+
+--- Training Stage 3/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.013378
+Epoch [2/5], Loss: 0.013113
+Epoch [3/5], Loss: 0.012313
+Epoch [4/5], Loss: 0.010373
+Epoch [5/5], Loss: 0.007055
+
+--- Training Stage 4/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.007644
+Epoch [2/5], Loss: 0.007603
+Epoch [3/5], Loss: 0.007357
+Epoch [4/5], Loss: 0.006320
+Epoch [5/5], Loss: 0.005322
+
+--- Training Stage 5/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.009695
+Epoch [2/5], Loss: 0.009589
+Epoch [3/5], Loss: 0.009219
+Epoch [4/5], Loss: 0.008468
+Epoch [5/5], Loss: 0.006984
+
+[+] Training complete for sigma=25/255, gamma=0.5, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=0.5, tau_init=0.05: 22.14 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma25_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..0e3479ca71690846ad2f9ca246c2e0c1a0112f5f
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma0p5_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph Model for sigma=25/255, gamma=0.5, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.008309
+Epoch [2/5], Loss: 0.006648
+Epoch [3/5], Loss: 0.003875
+Epoch [4/5], Loss: 0.002476
+Epoch [5/5], Loss: 0.002165
+
+--- Training Stage 2/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.007956
+Epoch [2/5], Loss: 0.007654
+Epoch [3/5], Loss: 0.006974
+Epoch [4/5], Loss: 0.006189
+Epoch [5/5], Loss: 0.005276
+
+--- Training Stage 3/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.012343
+Epoch [2/5], Loss: 0.011019
+Epoch [3/5], Loss: 0.008342
+Epoch [4/5], Loss: 0.004857
+Epoch [5/5], Loss: 0.003166
+
+--- Training Stage 4/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.006166
+Epoch [2/5], Loss: 0.006075
+Epoch [3/5], Loss: 0.005674
+Epoch [4/5], Loss: 0.005192
+Epoch [5/5], Loss: 0.004585
+
+--- Training Stage 5/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.009037
+Epoch [2/5], Loss: 0.008225
+Epoch [3/5], Loss: 0.006747
+Epoch [4/5], Loss: 0.004842
+Epoch [5/5], Loss: 0.003945
+
+[+] Training complete for sigma=25/255, gamma=0.5, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=0.5, tau_init=0.1: 24.30 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma25_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..15240866c739167271a6946c4e6b35d134669192
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma0p5_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph Model for sigma=25/255, gamma=0.5, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.008890
+Epoch [2/5], Loss: 0.006098
+Epoch [3/5], Loss: 0.003254
+Epoch [4/5], Loss: 0.002577
+Epoch [5/5], Loss: 0.002400
+
+--- Training Stage 2/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.007534
+Epoch [2/5], Loss: 0.006761
+Epoch [3/5], Loss: 0.006000
+Epoch [4/5], Loss: 0.005553
+Epoch [5/5], Loss: 0.005270
+
+--- Training Stage 3/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.012892
+Epoch [2/5], Loss: 0.009703
+Epoch [3/5], Loss: 0.005114
+Epoch [4/5], Loss: 0.003229
+Epoch [5/5], Loss: 0.002798
+
+--- Training Stage 4/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.006467
+Epoch [2/5], Loss: 0.005690
+Epoch [3/5], Loss: 0.004841
+Epoch [4/5], Loss: 0.004203
+Epoch [5/5], Loss: 0.003978
+
+--- Training Stage 5/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.008896
+Epoch [2/5], Loss: 0.006847
+Epoch [3/5], Loss: 0.004678
+Epoch [4/5], Loss: 0.003668
+Epoch [5/5], Loss: 0.003397
+
+[+] Training complete for sigma=25/255, gamma=0.5, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=0.5, tau_init=0.2: 24.60 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma25_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..a0d0f8d30e6c4465d87736ea8a0e69969c89c151
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph Model for sigma=25/255, gamma=1.0, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.008637
+Epoch [2/5], Loss: 0.008127
+Epoch [3/5], Loss: 0.006937
+Epoch [4/5], Loss: 0.004759
+Epoch [5/5], Loss: 0.002800
+
+--- Training Stage 2/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.006282
+Epoch [2/5], Loss: 0.006232
+Epoch [3/5], Loss: 0.005864
+Epoch [4/5], Loss: 0.005332
+Epoch [5/5], Loss: 0.004459
+
+--- Training Stage 3/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.009931
+Epoch [2/5], Loss: 0.009699
+Epoch [3/5], Loss: 0.009246
+Epoch [4/5], Loss: 0.008129
+Epoch [5/5], Loss: 0.006132
+
+--- Training Stage 4/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.007646
+Epoch [2/5], Loss: 0.007445
+Epoch [3/5], Loss: 0.007025
+Epoch [4/5], Loss: 0.006219
+Epoch [5/5], Loss: 0.005015
+
+--- Training Stage 5/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.007344
+Epoch [2/5], Loss: 0.007351
+Epoch [3/5], Loss: 0.007147
+Epoch [4/5], Loss: 0.006757
+Epoch [5/5], Loss: 0.006192
+
+[+] Training complete for sigma=25/255, gamma=1.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=1.0, tau_init=0.05: 22.50 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma25_gamma1_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..a5f35b60edfc09331f6c1b21d4c7d1aceea999ab
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma1p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph Model for sigma=25/255, gamma=1.0, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.008143
+Epoch [2/5], Loss: 0.006512
+Epoch [3/5], Loss: 0.003919
+Epoch [4/5], Loss: 0.002430
+Epoch [5/5], Loss: 0.002102
+
+--- Training Stage 2/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.007464
+Epoch [2/5], Loss: 0.007120
+Epoch [3/5], Loss: 0.006639
+Epoch [4/5], Loss: 0.005691
+Epoch [5/5], Loss: 0.004768
+
+--- Training Stage 3/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.010539
+Epoch [2/5], Loss: 0.009691
+Epoch [3/5], Loss: 0.007862
+Epoch [4/5], Loss: 0.005140
+Epoch [5/5], Loss: 0.003167
+
+--- Training Stage 4/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.004790
+Epoch [2/5], Loss: 0.004571
+Epoch [3/5], Loss: 0.004400
+Epoch [4/5], Loss: 0.004183
+Epoch [5/5], Loss: 0.003853
+
+--- Training Stage 5/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.007414
+Epoch [2/5], Loss: 0.006795
+Epoch [3/5], Loss: 0.005696
+Epoch [4/5], Loss: 0.004154
+Epoch [5/5], Loss: 0.003259
+
+[+] Training complete for sigma=25/255, gamma=1.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=1.0, tau_init=0.1: 24.97 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma25_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma1p0_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma1p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..f7bd73ccea6e4b0b1b27296e0ccc4d8a8137dcce
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma1p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph Model for sigma=25/255, gamma=1.0, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.008550
+Epoch [2/5], Loss: 0.006018
+Epoch [3/5], Loss: 0.003549
+Epoch [4/5], Loss: 0.002655
+Epoch [5/5], Loss: 0.002325
+
+--- Training Stage 2/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.006692
+Epoch [2/5], Loss: 0.005902
+Epoch [3/5], Loss: 0.005097
+Epoch [4/5], Loss: 0.004530
+Epoch [5/5], Loss: 0.004126
+
+--- Training Stage 3/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.007339
+Epoch [2/5], Loss: 0.005393
+Epoch [3/5], Loss: 0.003571
+Epoch [4/5], Loss: 0.002985
+Epoch [5/5], Loss: 0.002869
+
+--- Training Stage 4/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.004728
+Epoch [2/5], Loss: 0.004327
+Epoch [3/5], Loss: 0.003795
+Epoch [4/5], Loss: 0.003418
+Epoch [5/5], Loss: 0.003232
+
+--- Training Stage 5/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.005184
+Epoch [2/5], Loss: 0.004407
+Epoch [3/5], Loss: 0.003480
+Epoch [4/5], Loss: 0.003077
+Epoch [5/5], Loss: 0.002986
+
+[+] Training complete for sigma=25/255, gamma=1.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=1.0, tau_init=0.2: 25.26 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma25_gamma1_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma2p0_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma2p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..91ecddb408448182aceeb0125c2fbbc8bdf0415f
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma2p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph Model for sigma=25/255, gamma=2.0, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.008711
+Epoch [2/5], Loss: 0.008285
+Epoch [3/5], Loss: 0.007059
+Epoch [4/5], Loss: 0.004699
+Epoch [5/5], Loss: 0.002648
+
+--- Training Stage 2/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.006233
+Epoch [2/5], Loss: 0.006061
+Epoch [3/5], Loss: 0.005591
+Epoch [4/5], Loss: 0.004981
+Epoch [5/5], Loss: 0.004157
+
+--- Training Stage 3/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.008642
+Epoch [2/5], Loss: 0.008526
+Epoch [3/5], Loss: 0.008143
+Epoch [4/5], Loss: 0.007285
+Epoch [5/5], Loss: 0.005638
+
+--- Training Stage 4/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.006622
+Epoch [2/5], Loss: 0.006449
+Epoch [3/5], Loss: 0.006188
+Epoch [4/5], Loss: 0.005595
+Epoch [5/5], Loss: 0.004447
+
+--- Training Stage 5/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.005181
+Epoch [2/5], Loss: 0.005138
+Epoch [3/5], Loss: 0.005074
+Epoch [4/5], Loss: 0.004944
+Epoch [5/5], Loss: 0.004663
+
+[+] Training complete for sigma=25/255, gamma=2.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=2.0, tau_init=0.05: 23.49 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma25_gamma2_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma2p0_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma2p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..33b2c9e8588b4f862f6f6add6639c8a54be43f84
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma2p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph Model for sigma=25/255, gamma=2.0, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.008273
+Epoch [2/5], Loss: 0.006528
+Epoch [3/5], Loss: 0.003895
+Epoch [4/5], Loss: 0.002469
+Epoch [5/5], Loss: 0.002179
+
+--- Training Stage 2/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.006511
+Epoch [2/5], Loss: 0.006136
+Epoch [3/5], Loss: 0.005513
+Epoch [4/5], Loss: 0.004932
+Epoch [5/5], Loss: 0.004358
+
+--- Training Stage 3/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.008848
+Epoch [2/5], Loss: 0.008240
+Epoch [3/5], Loss: 0.006915
+Epoch [4/5], Loss: 0.004883
+Epoch [5/5], Loss: 0.003178
+
+--- Training Stage 4/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.004307
+Epoch [2/5], Loss: 0.004248
+Epoch [3/5], Loss: 0.003942
+Epoch [4/5], Loss: 0.003524
+Epoch [5/5], Loss: 0.003181
+
+--- Training Stage 5/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.004989
+Epoch [2/5], Loss: 0.004716
+Epoch [3/5], Loss: 0.004261
+Epoch [4/5], Loss: 0.003496
+Epoch [5/5], Loss: 0.002860
+
+[+] Training complete for sigma=25/255, gamma=2.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=2.0, tau_init=0.1: 25.42 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma25_gamma2_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma2p0_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma2p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..90039d3e1f9df6749d4f148fdf66a7bf34428da8
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma25_gamma2p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph Model for sigma=25/255, gamma=2.0, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.007449
+Epoch [2/5], Loss: 0.004552
+Epoch [3/5], Loss: 0.002581
+Epoch [4/5], Loss: 0.002151
+Epoch [5/5], Loss: 0.002075
+
+--- Training Stage 2/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.005236
+Epoch [2/5], Loss: 0.004653
+Epoch [3/5], Loss: 0.004102
+Epoch [4/5], Loss: 0.003646
+Epoch [5/5], Loss: 0.003380
+
+--- Training Stage 3/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.005769
+Epoch [2/5], Loss: 0.004957
+Epoch [3/5], Loss: 0.003748
+Epoch [4/5], Loss: 0.002826
+Epoch [5/5], Loss: 0.002340
+
+--- Training Stage 4/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.003001
+Epoch [2/5], Loss: 0.002764
+Epoch [3/5], Loss: 0.002551
+Epoch [4/5], Loss: 0.002376
+Epoch [5/5], Loss: 0.002194
+
+--- Training Stage 5/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.002829
+Epoch [2/5], Loss: 0.002584
+Epoch [3/5], Loss: 0.002296
+Epoch [4/5], Loss: 0.002106
+Epoch [5/5], Loss: 0.002142
+
+[+] Training complete for sigma=25/255, gamma=2.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=25/255, gamma=2.0, tau_init=0.2: 26.24 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma25_gamma2_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..25bfae7c309e37b142a72c009512a3f792155846
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma0p5_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph Model for sigma=50/255, gamma=0.5, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.030900
+Epoch [2/5], Loss: 0.028999
+Epoch [3/5], Loss: 0.023385
+Epoch [4/5], Loss: 0.012874
+Epoch [5/5], Loss: 0.005616
+
+--- Training Stage 2/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.027451
+Epoch [2/5], Loss: 0.027128
+Epoch [3/5], Loss: 0.026566
+Epoch [4/5], Loss: 0.025447
+Epoch [5/5], Loss: 0.023751
+
+--- Training Stage 3/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.075638
+Epoch [2/5], Loss: 0.073121
+Epoch [3/5], Loss: 0.067214
+Epoch [4/5], Loss: 0.052132
+Epoch [5/5], Loss: 0.024914
+
+--- Training Stage 4/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.017479
+Epoch [2/5], Loss: 0.017259
+Epoch [3/5], Loss: 0.016746
+Epoch [4/5], Loss: 0.015300
+Epoch [5/5], Loss: 0.013069
+
+--- Training Stage 5/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.030029
+Epoch [2/5], Loss: 0.029719
+Epoch [3/5], Loss: 0.028476
+Epoch [4/5], Loss: 0.025074
+Epoch [5/5], Loss: 0.019219
+
+[+] Training complete for sigma=50/255, gamma=0.5, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=0.5, tau_init=0.05: 18.20 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma50_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..612c2646beb13cfc7607900195b2c2a6cab374a0
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma0p5_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph Model for sigma=50/255, gamma=0.5, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.030941
+Epoch [2/5], Loss: 0.026448
+Epoch [3/5], Loss: 0.016310
+Epoch [4/5], Loss: 0.007655
+Epoch [5/5], Loss: 0.005340
+
+--- Training Stage 2/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.027628
+Epoch [2/5], Loss: 0.026804
+Epoch [3/5], Loss: 0.025562
+Epoch [4/5], Loss: 0.023431
+Epoch [5/5], Loss: 0.021151
+
+--- Training Stage 3/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.060468
+Epoch [2/5], Loss: 0.052950
+Epoch [3/5], Loss: 0.036025
+Epoch [4/5], Loss: 0.015476
+Epoch [5/5], Loss: 0.008151
+
+--- Training Stage 4/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.024130
+Epoch [2/5], Loss: 0.023373
+Epoch [3/5], Loss: 0.021945
+Epoch [4/5], Loss: 0.019153
+Epoch [5/5], Loss: 0.016073
+
+--- Training Stage 5/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.041901
+Epoch [2/5], Loss: 0.038446
+Epoch [3/5], Loss: 0.029570
+Epoch [4/5], Loss: 0.016012
+Epoch [5/5], Loss: 0.009819
+
+[+] Training complete for sigma=50/255, gamma=0.5, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=0.5, tau_init=0.1: 20.78 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma50_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..daf77fa68d97226dafc7558f953cdc2c72984d27
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma0p5_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph Model for sigma=50/255, gamma=0.5, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.020986
+Epoch [2/5], Loss: 0.011947
+Epoch [3/5], Loss: 0.007624
+Epoch [4/5], Loss: 0.005781
+Epoch [5/5], Loss: 0.005098
+
+--- Training Stage 2/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.025693
+Epoch [2/5], Loss: 0.024086
+Epoch [3/5], Loss: 0.021499
+Epoch [4/5], Loss: 0.018870
+Epoch [5/5], Loss: 0.016819
+
+--- Training Stage 3/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.041218
+Epoch [2/5], Loss: 0.025482
+Epoch [3/5], Loss: 0.010252
+Epoch [4/5], Loss: 0.006866
+Epoch [5/5], Loss: 0.006213
+
+--- Training Stage 4/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.018635
+Epoch [2/5], Loss: 0.017473
+Epoch [3/5], Loss: 0.015813
+Epoch [4/5], Loss: 0.014269
+Epoch [5/5], Loss: 0.013143
+
+--- Training Stage 5/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.033617
+Epoch [2/5], Loss: 0.024713
+Epoch [3/5], Loss: 0.012731
+Epoch [4/5], Loss: 0.008810
+Epoch [5/5], Loss: 0.008212
+
+[+] Training complete for sigma=50/255, gamma=0.5, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=0.5, tau_init=0.2: 21.25 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma50_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..9136ac507ad66122d1b30739f8cd627068a46174
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph Model for sigma=50/255, gamma=1.0, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.031006
+Epoch [2/5], Loss: 0.029533
+Epoch [3/5], Loss: 0.024987
+Epoch [4/5], Loss: 0.015108
+Epoch [5/5], Loss: 0.006302
+
+--- Training Stage 2/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.025881
+Epoch [2/5], Loss: 0.025371
+Epoch [3/5], Loss: 0.024786
+Epoch [4/5], Loss: 0.023521
+Epoch [5/5], Loss: 0.021405
+
+--- Training Stage 3/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.064330
+Epoch [2/5], Loss: 0.062336
+Epoch [3/5], Loss: 0.057731
+Epoch [4/5], Loss: 0.046672
+Epoch [5/5], Loss: 0.026261
+
+--- Training Stage 4/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.016330
+Epoch [2/5], Loss: 0.016136
+Epoch [3/5], Loss: 0.015160
+Epoch [4/5], Loss: 0.013311
+Epoch [5/5], Loss: 0.011174
+
+--- Training Stage 5/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.024249
+Epoch [2/5], Loss: 0.023669
+Epoch [3/5], Loss: 0.022742
+Epoch [4/5], Loss: 0.021294
+Epoch [5/5], Loss: 0.018444
+
+[+] Training complete for sigma=50/255, gamma=1.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=1.0, tau_init=0.05: 17.95 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma50_gamma1_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..8d1b8bfed6e917bf113ed70227da737b3c6e204c
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma1p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph Model for sigma=50/255, gamma=1.0, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.030054
+Epoch [2/5], Loss: 0.024645
+Epoch [3/5], Loss: 0.013525
+Epoch [4/5], Loss: 0.006018
+Epoch [5/5], Loss: 0.004881
+
+--- Training Stage 2/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.025874
+Epoch [2/5], Loss: 0.024811
+Epoch [3/5], Loss: 0.023390
+Epoch [4/5], Loss: 0.021188
+Epoch [5/5], Loss: 0.019168
+
+--- Training Stage 3/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.053374
+Epoch [2/5], Loss: 0.047383
+Epoch [3/5], Loss: 0.035512
+Epoch [4/5], Loss: 0.018475
+Epoch [5/5], Loss: 0.009276
+
+--- Training Stage 4/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.021587
+Epoch [2/5], Loss: 0.020321
+Epoch [3/5], Loss: 0.018118
+Epoch [4/5], Loss: 0.015653
+Epoch [5/5], Loss: 0.013602
+
+--- Training Stage 5/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.035154
+Epoch [2/5], Loss: 0.032126
+Epoch [3/5], Loss: 0.025631
+Epoch [4/5], Loss: 0.014860
+Epoch [5/5], Loss: 0.008859
+
+[+] Training complete for sigma=50/255, gamma=1.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=1.0, tau_init=0.1: 20.90 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma50_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma1p0_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma1p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..4556add38a13faac8b5fb649158727e561446633
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma1p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph Model for sigma=50/255, gamma=1.0, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.028070
+Epoch [2/5], Loss: 0.016520
+Epoch [3/5], Loss: 0.008059
+Epoch [4/5], Loss: 0.005429
+Epoch [5/5], Loss: 0.004799
+
+--- Training Stage 2/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.022654
+Epoch [2/5], Loss: 0.021165
+Epoch [3/5], Loss: 0.019017
+Epoch [4/5], Loss: 0.017076
+Epoch [5/5], Loss: 0.015324
+
+--- Training Stage 3/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.037168
+Epoch [2/5], Loss: 0.027792
+Epoch [3/5], Loss: 0.014818
+Epoch [4/5], Loss: 0.007630
+Epoch [5/5], Loss: 0.006250
+
+--- Training Stage 4/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.014395
+Epoch [2/5], Loss: 0.013314
+Epoch [3/5], Loss: 0.011844
+Epoch [4/5], Loss: 0.010340
+Epoch [5/5], Loss: 0.009093
+
+--- Training Stage 5/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.017169
+Epoch [2/5], Loss: 0.013409
+Epoch [3/5], Loss: 0.008978
+Epoch [4/5], Loss: 0.006878
+Epoch [5/5], Loss: 0.006614
+
+[+] Training complete for sigma=50/255, gamma=1.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=1.0, tau_init=0.2: 22.14 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma50_gamma1_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma2p0_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma2p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..d1174dd438c4224e8042aae6cf3e5d486ca373b3
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma2p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph Model for sigma=50/255, gamma=2.0, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.031045
+Epoch [2/5], Loss: 0.029586
+Epoch [3/5], Loss: 0.025540
+Epoch [4/5], Loss: 0.016343
+Epoch [5/5], Loss: 0.007026
+
+--- Training Stage 2/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.020356
+Epoch [2/5], Loss: 0.019705
+Epoch [3/5], Loss: 0.019023
+Epoch [4/5], Loss: 0.017640
+Epoch [5/5], Loss: 0.015500
+
+--- Training Stage 3/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.042025
+Epoch [2/5], Loss: 0.040794
+Epoch [3/5], Loss: 0.038429
+Epoch [4/5], Loss: 0.032930
+Epoch [5/5], Loss: 0.021944
+
+--- Training Stage 4/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.017795
+Epoch [2/5], Loss: 0.017177
+Epoch [3/5], Loss: 0.015880
+Epoch [4/5], Loss: 0.013172
+Epoch [5/5], Loss: 0.009008
+
+--- Training Stage 5/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.014620
+Epoch [2/5], Loss: 0.014273
+Epoch [3/5], Loss: 0.013976
+Epoch [4/5], Loss: 0.013475
+Epoch [5/5], Loss: 0.012729
+
+[+] Training complete for sigma=50/255, gamma=2.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=2.0, tau_init=0.05: 19.16 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma50_gamma2_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma2p0_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma2p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..90cef61b43b5c3b4c527d35eac6894b234f5d845
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma2p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph Model for sigma=50/255, gamma=2.0, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.030670
+Epoch [2/5], Loss: 0.026390
+Epoch [3/5], Loss: 0.016046
+Epoch [4/5], Loss: 0.006697
+Epoch [5/5], Loss: 0.004960
+
+--- Training Stage 2/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.021823
+Epoch [2/5], Loss: 0.020716
+Epoch [3/5], Loss: 0.019299
+Epoch [4/5], Loss: 0.017886
+Epoch [5/5], Loss: 0.016125
+
+--- Training Stage 3/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.039376
+Epoch [2/5], Loss: 0.035348
+Epoch [3/5], Loss: 0.027727
+Epoch [4/5], Loss: 0.015563
+Epoch [5/5], Loss: 0.007520
+
+--- Training Stage 4/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.013571
+Epoch [2/5], Loss: 0.013262
+Epoch [3/5], Loss: 0.012516
+Epoch [4/5], Loss: 0.011562
+Epoch [5/5], Loss: 0.010552
+
+--- Training Stage 5/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.020101
+Epoch [2/5], Loss: 0.018587
+Epoch [3/5], Loss: 0.016217
+Epoch [4/5], Loss: 0.012254
+Epoch [5/5], Loss: 0.008283
+
+[+] Training complete for sigma=50/255, gamma=2.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=2.0, tau_init=0.1: 21.78 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma50_gamma2_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma2p0_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma2p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..ed4afef0fe1409cb3a6e4d69da0ddcc22bb0d60e
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma50_gamma2p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph Model for sigma=50/255, gamma=2.0, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.028365
+Epoch [2/5], Loss: 0.018262
+Epoch [3/5], Loss: 0.008805
+Epoch [4/5], Loss: 0.006045
+Epoch [5/5], Loss: 0.005170
+
+--- Training Stage 2/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.017293
+Epoch [2/5], Loss: 0.015978
+Epoch [3/5], Loss: 0.013969
+Epoch [4/5], Loss: 0.012311
+Epoch [5/5], Loss: 0.010979
+
+--- Training Stage 3/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.020114
+Epoch [2/5], Loss: 0.015613
+Epoch [3/5], Loss: 0.009610
+Epoch [4/5], Loss: 0.005931
+Epoch [5/5], Loss: 0.005331
+
+--- Training Stage 4/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.008995
+Epoch [2/5], Loss: 0.008361
+Epoch [3/5], Loss: 0.007600
+Epoch [4/5], Loss: 0.006724
+Epoch [5/5], Loss: 0.006233
+
+--- Training Stage 5/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.009219
+Epoch [2/5], Loss: 0.007997
+Epoch [3/5], Loss: 0.006440
+Epoch [4/5], Loss: 0.005493
+Epoch [5/5], Loss: 0.005295
+
+[+] Training complete for sigma=50/255, gamma=2.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=50/255, gamma=2.0, tau_init=0.2: 22.91 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma50_gamma2_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..839900be6f79b0cd8631ea76fb086f7a75bf1cd5
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma0p5_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph Model for sigma=75/255, gamma=0.5, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.059556
+Epoch [2/5], Loss: 0.056250
+Epoch [3/5], Loss: 0.046103
+Epoch [4/5], Loss: 0.026477
+Epoch [5/5], Loss: 0.010931
+
+--- Training Stage 2/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.051579
+Epoch [2/5], Loss: 0.051026
+Epoch [3/5], Loss: 0.050362
+Epoch [4/5], Loss: 0.048448
+Epoch [5/5], Loss: 0.044780
+
+--- Training Stage 3/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.143901
+Epoch [2/5], Loss: 0.138918
+Epoch [3/5], Loss: 0.127090
+Epoch [4/5], Loss: 0.097176
+Epoch [5/5], Loss: 0.044413
+
+--- Training Stage 4/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.026211
+Epoch [2/5], Loss: 0.025680
+Epoch [3/5], Loss: 0.024981
+Epoch [4/5], Loss: 0.023380
+Epoch [5/5], Loss: 0.020810
+
+--- Training Stage 5/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.051620
+Epoch [2/5], Loss: 0.050541
+Epoch [3/5], Loss: 0.048388
+Epoch [4/5], Loss: 0.042316
+Epoch [5/5], Loss: 0.030668
+
+[+] Training complete for sigma=75/255, gamma=0.5, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=0.5, tau_init=0.05: 16.29 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma75_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..2487f5ac33f21fbff288823e7b0d08f7b9674019
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma0p5_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph Model for sigma=75/255, gamma=0.5, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.057520
+Epoch [2/5], Loss: 0.046649
+Epoch [3/5], Loss: 0.024754
+Epoch [4/5], Loss: 0.010371
+Epoch [5/5], Loss: 0.008449
+
+--- Training Stage 2/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.055397
+Epoch [2/5], Loss: 0.054221
+Epoch [3/5], Loss: 0.052397
+Epoch [4/5], Loss: 0.048896
+Epoch [5/5], Loss: 0.044480
+
+--- Training Stage 3/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.133167
+Epoch [2/5], Loss: 0.118788
+Epoch [3/5], Loss: 0.086235
+Epoch [4/5], Loss: 0.038747
+Epoch [5/5], Loss: 0.014691
+
+--- Training Stage 4/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.046069
+Epoch [2/5], Loss: 0.044652
+Epoch [3/5], Loss: 0.041652
+Epoch [4/5], Loss: 0.036409
+Epoch [5/5], Loss: 0.031514
+
+--- Training Stage 5/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.089925
+Epoch [2/5], Loss: 0.081151
+Epoch [3/5], Loss: 0.058720
+Epoch [4/5], Loss: 0.028018
+Epoch [5/5], Loss: 0.017432
+
+[+] Training complete for sigma=75/255, gamma=0.5, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=0.5, tau_init=0.1: 18.23 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma75_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..5a61343f3fc8e88c8de37ec6063832d13802294b
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma0p5_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph Model for sigma=75/255, gamma=0.5, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.041587
+Epoch [2/5], Loss: 0.020475
+Epoch [3/5], Loss: 0.011312
+Epoch [4/5], Loss: 0.009921
+Epoch [5/5], Loss: 0.009168
+
+--- Training Stage 2/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.050697
+Epoch [2/5], Loss: 0.047964
+Epoch [3/5], Loss: 0.045060
+Epoch [4/5], Loss: 0.041559
+Epoch [5/5], Loss: 0.037282
+
+--- Training Stage 3/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.106774
+Epoch [2/5], Loss: 0.077309
+Epoch [3/5], Loss: 0.035648
+Epoch [4/5], Loss: 0.015385
+Epoch [5/5], Loss: 0.012497
+
+--- Training Stage 4/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.039474
+Epoch [2/5], Loss: 0.035355
+Epoch [3/5], Loss: 0.029321
+Epoch [4/5], Loss: 0.024600
+Epoch [5/5], Loss: 0.022639
+
+--- Training Stage 5/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.061554
+Epoch [2/5], Loss: 0.043614
+Epoch [3/5], Loss: 0.022798
+Epoch [4/5], Loss: 0.017057
+Epoch [5/5], Loss: 0.015473
+
+[+] Training complete for sigma=75/255, gamma=0.5, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=0.5, tau_init=0.2: 18.72 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma75_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..0e6f1f9aea07756c28aa03ff5a32d5d6912f7223
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph Model for sigma=75/255, gamma=1.0, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.060180
+Epoch [2/5], Loss: 0.057182
+Epoch [3/5], Loss: 0.047781
+Epoch [4/5], Loss: 0.027547
+Epoch [5/5], Loss: 0.010661
+
+--- Training Stage 2/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.051374
+Epoch [2/5], Loss: 0.050601
+Epoch [3/5], Loss: 0.049563
+Epoch [4/5], Loss: 0.048080
+Epoch [5/5], Loss: 0.045883
+
+--- Training Stage 3/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.147072
+Epoch [2/5], Loss: 0.143351
+Epoch [3/5], Loss: 0.134447
+Epoch [4/5], Loss: 0.112192
+Epoch [5/5], Loss: 0.065548
+
+--- Training Stage 4/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.037185
+Epoch [2/5], Loss: 0.035531
+Epoch [3/5], Loss: 0.031050
+Epoch [4/5], Loss: 0.022537
+Epoch [5/5], Loss: 0.016004
+
+--- Training Stage 5/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.046213
+Epoch [2/5], Loss: 0.045609
+Epoch [3/5], Loss: 0.044319
+Epoch [4/5], Loss: 0.041587
+Epoch [5/5], Loss: 0.037013
+
+[+] Training complete for sigma=75/255, gamma=1.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=1.0, tau_init=0.05: 14.78 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma75_gamma1_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..17fe95b9bf05addae5f6379a8d46ebb465186826
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma1p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph Model for sigma=75/255, gamma=1.0, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.057783
+Epoch [2/5], Loss: 0.048094
+Epoch [3/5], Loss: 0.028370
+Epoch [4/5], Loss: 0.011687
+Epoch [5/5], Loss: 0.008579
+
+--- Training Stage 2/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.049812
+Epoch [2/5], Loss: 0.048315
+Epoch [3/5], Loss: 0.046221
+Epoch [4/5], Loss: 0.043160
+Epoch [5/5], Loss: 0.039963
+
+--- Training Stage 3/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.113858
+Epoch [2/5], Loss: 0.100318
+Epoch [3/5], Loss: 0.073042
+Epoch [4/5], Loss: 0.036069
+Epoch [5/5], Loss: 0.015158
+
+--- Training Stage 4/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.036715
+Epoch [2/5], Loss: 0.035763
+Epoch [3/5], Loss: 0.033691
+Epoch [4/5], Loss: 0.030363
+Epoch [5/5], Loss: 0.025596
+
+--- Training Stage 5/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.061088
+Epoch [2/5], Loss: 0.055392
+Epoch [3/5], Loss: 0.042543
+Epoch [4/5], Loss: 0.023412
+Epoch [5/5], Loss: 0.014109
+
+[+] Training complete for sigma=75/255, gamma=1.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=1.0, tau_init=0.1: 19.25 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma75_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma1p0_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma1p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..5a6d087a8b2c532e6f53ee168befb8c598f60b20
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma1p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph Model for sigma=75/255, gamma=1.0, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.060327
+Epoch [2/5], Loss: 0.038839
+Epoch [3/5], Loss: 0.015812
+Epoch [4/5], Loss: 0.009357
+Epoch [5/5], Loss: 0.008275
+
+--- Training Stage 2/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.043313
+Epoch [2/5], Loss: 0.041143
+Epoch [3/5], Loss: 0.038634
+Epoch [4/5], Loss: 0.035979
+Epoch [5/5], Loss: 0.032952
+
+--- Training Stage 3/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.075939
+Epoch [2/5], Loss: 0.052215
+Epoch [3/5], Loss: 0.023932
+Epoch [4/5], Loss: 0.012926
+Epoch [5/5], Loss: 0.010811
+
+--- Training Stage 4/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.030617
+Epoch [2/5], Loss: 0.027658
+Epoch [3/5], Loss: 0.023912
+Epoch [4/5], Loss: 0.020295
+Epoch [5/5], Loss: 0.018166
+
+--- Training Stage 5/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.042246
+Epoch [2/5], Loss: 0.034866
+Epoch [3/5], Loss: 0.022125
+Epoch [4/5], Loss: 0.013411
+Epoch [5/5], Loss: 0.011905
+
+[+] Training complete for sigma=75/255, gamma=1.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=1.0, tau_init=0.2: 19.83 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma75_gamma1_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma2p0_tau0p05.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma2p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..1390150862fa4be3b0e7b03879deba3901eee48d
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma2p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph Model for sigma=75/255, gamma=2.0, tau_init=0.05...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.060092
+Epoch [2/5], Loss: 0.057102
+Epoch [3/5], Loss: 0.048456
+Epoch [4/5], Loss: 0.029617
+Epoch [5/5], Loss: 0.011808
+
+--- Training Stage 2/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.043021
+Epoch [2/5], Loss: 0.041873
+Epoch [3/5], Loss: 0.040396
+Epoch [4/5], Loss: 0.038389
+Epoch [5/5], Loss: 0.035850
+
+--- Training Stage 3/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.106276
+Epoch [2/5], Loss: 0.102790
+Epoch [3/5], Loss: 0.095399
+Epoch [4/5], Loss: 0.078721
+Epoch [5/5], Loss: 0.047844
+
+--- Training Stage 4/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.029527
+Epoch [2/5], Loss: 0.028619
+Epoch [3/5], Loss: 0.025533
+Epoch [4/5], Loss: 0.019397
+Epoch [5/5], Loss: 0.013491
+
+--- Training Stage 5/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], Loss: 0.032052
+Epoch [2/5], Loss: 0.031614
+Epoch [3/5], Loss: 0.030678
+Epoch [4/5], Loss: 0.029391
+Epoch [5/5], Loss: 0.027223
+
+[+] Training complete for sigma=75/255, gamma=2.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=2.0, tau_init=0.05: 15.90 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma75_gamma2_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma2p0_tau0p1.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma2p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..162a50cad3522759d5e24694ae7b87b8c75b217a
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma2p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph Model for sigma=75/255, gamma=2.0, tau_init=0.1...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.058843
+Epoch [2/5], Loss: 0.050651
+Epoch [3/5], Loss: 0.031596
+Epoch [4/5], Loss: 0.011903
+Epoch [5/5], Loss: 0.008067
+
+--- Training Stage 2/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.043339
+Epoch [2/5], Loss: 0.041808
+Epoch [3/5], Loss: 0.039475
+Epoch [4/5], Loss: 0.036717
+Epoch [5/5], Loss: 0.033726
+
+--- Training Stage 3/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.086959
+Epoch [2/5], Loss: 0.079583
+Epoch [3/5], Loss: 0.063539
+Epoch [4/5], Loss: 0.037106
+Epoch [5/5], Loss: 0.016107
+
+--- Training Stage 4/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.027377
+Epoch [2/5], Loss: 0.026269
+Epoch [3/5], Loss: 0.024631
+Epoch [4/5], Loss: 0.022283
+Epoch [5/5], Loss: 0.019335
+
+--- Training Stage 5/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], Loss: 0.038594
+Epoch [2/5], Loss: 0.035258
+Epoch [3/5], Loss: 0.029411
+Epoch [4/5], Loss: 0.020725
+Epoch [5/5], Loss: 0.014534
+
+[+] Training complete for sigma=75/255, gamma=2.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=2.0, tau_init=0.1: 19.10 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma75_gamma2_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma2p0_tau0p2.log b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma2p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..35affa0fd78687159eadf7777a2296e7f6479062
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_mlp_wave_sigma75_gamma2p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph Model for sigma=75/255, gamma=2.0, tau_init=0.2...
+
+--- Training Stage 1/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.051018
+Epoch [2/5], Loss: 0.030241
+Epoch [3/5], Loss: 0.012521
+Epoch [4/5], Loss: 0.009121
+Epoch [5/5], Loss: 0.008234
+
+--- Training Stage 2/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.033224
+Epoch [2/5], Loss: 0.030594
+Epoch [3/5], Loss: 0.027769
+Epoch [4/5], Loss: 0.024722
+Epoch [5/5], Loss: 0.022254
+
+--- Training Stage 3/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.043321
+Epoch [2/5], Loss: 0.031898
+Epoch [3/5], Loss: 0.017500
+Epoch [4/5], Loss: 0.009901
+Epoch [5/5], Loss: 0.008724
+
+--- Training Stage 4/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.016304
+Epoch [2/5], Loss: 0.015187
+Epoch [3/5], Loss: 0.013752
+Epoch [4/5], Loss: 0.012280
+Epoch [5/5], Loss: 0.011240
+
+--- Training Stage 5/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], Loss: 0.017523
+Epoch [2/5], Loss: 0.014930
+Epoch [3/5], Loss: 0.011310
+Epoch [4/5], Loss: 0.009108
+Epoch [5/5], Loss: 0.008838
+
+[+] Training complete for sigma=75/255, gamma=2.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final Model PSNR on BSD68 at sigma=75/255, gamma=2.0, tau_init=0.2: 21.01 dB
+[+] Saved checkpoint: model_5stages_waveTrue_sigma75_gamma2_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..75a1141a577c9ef5a7068cafa5dcb7e3dfb633d0
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma0p5_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=15/255, gamma=0.5, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001364
+Epoch [2/5], loss: 0.000800
+Epoch [3/5], loss: 0.000752
+Epoch [4/5], loss: 0.000736
+Epoch [5/5], loss: 0.000724
+
+--- Training stage 2/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.000800
+Epoch [2/5], loss: 0.000723
+Epoch [3/5], loss: 0.000718
+Epoch [4/5], loss: 0.000715
+Epoch [5/5], loss: 0.000709
+
+--- Training stage 3/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.000775
+Epoch [2/5], loss: 0.000721
+Epoch [3/5], loss: 0.000715
+Epoch [4/5], loss: 0.000719
+Epoch [5/5], loss: 0.000715
+
+--- Training stage 4/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.000824
+Epoch [2/5], loss: 0.000724
+Epoch [3/5], loss: 0.000715
+Epoch [4/5], loss: 0.000709
+Epoch [5/5], loss: 0.000703
+
+--- Training stage 5/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.000761
+Epoch [2/5], loss: 0.000725
+Epoch [3/5], loss: 0.000716
+Epoch [4/5], loss: 0.000715
+Epoch [5/5], loss: 0.000706
+
+[+] Training complete for sigma=15/255, gamma=0.5, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 30.55 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma15_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..76102cab5b9baeaae594f10439374a7e0bd4516a
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma0p5_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=15/255, gamma=0.5, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001733
+Epoch [2/5], loss: 0.000871
+Epoch [3/5], loss: 0.000777
+Epoch [4/5], loss: 0.000749
+Epoch [5/5], loss: 0.000732
+
+--- Training stage 2/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001022
+Epoch [2/5], loss: 0.000772
+Epoch [3/5], loss: 0.000736
+Epoch [4/5], loss: 0.000721
+Epoch [5/5], loss: 0.000720
+
+--- Training stage 3/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001029
+Epoch [2/5], loss: 0.000775
+Epoch [3/5], loss: 0.000734
+Epoch [4/5], loss: 0.000718
+Epoch [5/5], loss: 0.000719
+
+--- Training stage 4/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.000965
+Epoch [2/5], loss: 0.000759
+Epoch [3/5], loss: 0.000731
+Epoch [4/5], loss: 0.000725
+Epoch [5/5], loss: 0.000720
+
+--- Training stage 5/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001020
+Epoch [2/5], loss: 0.000759
+Epoch [3/5], loss: 0.000742
+Epoch [4/5], loss: 0.000731
+Epoch [5/5], loss: 0.000720
+
+[+] Training complete for sigma=15/255, gamma=0.5, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 30.49 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma15_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..cbefbbfc6db0a2c451d5e43b250e8ac623140ebb
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma0p5_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=15/255, gamma=0.5, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.003579
+Epoch [2/5], loss: 0.001022
+Epoch [3/5], loss: 0.000813
+Epoch [4/5], loss: 0.000767
+Epoch [5/5], loss: 0.000746
+
+--- Training stage 2/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001544
+Epoch [2/5], loss: 0.000861
+Epoch [3/5], loss: 0.000788
+Epoch [4/5], loss: 0.000752
+Epoch [5/5], loss: 0.000742
+
+--- Training stage 3/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001368
+Epoch [2/5], loss: 0.000824
+Epoch [3/5], loss: 0.000768
+Epoch [4/5], loss: 0.000744
+Epoch [5/5], loss: 0.000740
+
+--- Training stage 4/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001580
+Epoch [2/5], loss: 0.000823
+Epoch [3/5], loss: 0.000763
+Epoch [4/5], loss: 0.000745
+Epoch [5/5], loss: 0.000746
+
+--- Training stage 5/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001526
+Epoch [2/5], loss: 0.000861
+Epoch [3/5], loss: 0.000791
+Epoch [4/5], loss: 0.000764
+Epoch [5/5], loss: 0.000751
+
+[+] Training complete for sigma=15/255, gamma=0.5, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 30.41 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma15_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..cdbd3ff9441194b0db0084c6629d8809c1f05ce5
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=15/255, gamma=1.0, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001549
+Epoch [2/5], loss: 0.000811
+Epoch [3/5], loss: 0.000764
+Epoch [4/5], loss: 0.000742
+Epoch [5/5], loss: 0.000728
+
+--- Training stage 2/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.000781
+Epoch [2/5], loss: 0.000724
+Epoch [3/5], loss: 0.000719
+Epoch [4/5], loss: 0.000712
+Epoch [5/5], loss: 0.000720
+
+--- Training stage 3/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.000777
+Epoch [2/5], loss: 0.000718
+Epoch [3/5], loss: 0.000722
+Epoch [4/5], loss: 0.000703
+Epoch [5/5], loss: 0.000707
+
+--- Training stage 4/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.000766
+Epoch [2/5], loss: 0.000716
+Epoch [3/5], loss: 0.000715
+Epoch [4/5], loss: 0.000710
+Epoch [5/5], loss: 0.000710
+
+--- Training stage 5/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.000772
+Epoch [2/5], loss: 0.000721
+Epoch [3/5], loss: 0.000716
+Epoch [4/5], loss: 0.000706
+Epoch [5/5], loss: 0.000719
+
+[+] Training complete for sigma=15/255, gamma=1.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 30.55 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma15_gamma1_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..8ba6769c5fcdbab9f42ac9c8001861725056f920
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma1p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=15/255, gamma=1.0, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.002172
+Epoch [2/5], loss: 0.000919
+Epoch [3/5], loss: 0.000805
+Epoch [4/5], loss: 0.000765
+Epoch [5/5], loss: 0.000745
+
+--- Training stage 2/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.000962
+Epoch [2/5], loss: 0.000757
+Epoch [3/5], loss: 0.000744
+Epoch [4/5], loss: 0.000723
+Epoch [5/5], loss: 0.000725
+
+--- Training stage 3/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.000991
+Epoch [2/5], loss: 0.000785
+Epoch [3/5], loss: 0.000750
+Epoch [4/5], loss: 0.000747
+Epoch [5/5], loss: 0.000728
+
+--- Training stage 4/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.000930
+Epoch [2/5], loss: 0.000762
+Epoch [3/5], loss: 0.000736
+Epoch [4/5], loss: 0.000732
+Epoch [5/5], loss: 0.000725
+
+--- Training stage 5/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.000873
+Epoch [2/5], loss: 0.000742
+Epoch [3/5], loss: 0.000732
+Epoch [4/5], loss: 0.000730
+Epoch [5/5], loss: 0.000720
+
+[+] Training complete for sigma=15/255, gamma=1.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 30.49 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma15_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma1p0_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma1p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..5e1e757dfdb460cafe844b64015b41f457766edb
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma1p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=15/255, gamma=1.0, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.003635
+Epoch [2/5], loss: 0.000999
+Epoch [3/5], loss: 0.000796
+Epoch [4/5], loss: 0.000760
+Epoch [5/5], loss: 0.000756
+
+--- Training stage 2/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001407
+Epoch [2/5], loss: 0.000832
+Epoch [3/5], loss: 0.000761
+Epoch [4/5], loss: 0.000739
+Epoch [5/5], loss: 0.000720
+
+--- Training stage 3/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001592
+Epoch [2/5], loss: 0.000830
+Epoch [3/5], loss: 0.000762
+Epoch [4/5], loss: 0.000753
+Epoch [5/5], loss: 0.000733
+
+--- Training stage 4/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001324
+Epoch [2/5], loss: 0.000795
+Epoch [3/5], loss: 0.000752
+Epoch [4/5], loss: 0.000738
+Epoch [5/5], loss: 0.000730
+
+--- Training stage 5/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001778
+Epoch [2/5], loss: 0.000870
+Epoch [3/5], loss: 0.000774
+Epoch [4/5], loss: 0.000736
+Epoch [5/5], loss: 0.000739
+
+[+] Training complete for sigma=15/255, gamma=1.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 30.44 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma15_gamma1_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma2p0_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma2p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..c32ac7e46205847257c84b80f379972f7fa218cb
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma2p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=15/255, gamma=2.0, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001599
+Epoch [2/5], loss: 0.000830
+Epoch [3/5], loss: 0.000770
+Epoch [4/5], loss: 0.000747
+Epoch [5/5], loss: 0.000744
+
+--- Training stage 2/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.000768
+Epoch [2/5], loss: 0.000722
+Epoch [3/5], loss: 0.000726
+Epoch [4/5], loss: 0.000721
+Epoch [5/5], loss: 0.000721
+
+--- Training stage 3/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.000777
+Epoch [2/5], loss: 0.000722
+Epoch [3/5], loss: 0.000719
+Epoch [4/5], loss: 0.000712
+Epoch [5/5], loss: 0.000720
+
+--- Training stage 4/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.000752
+Epoch [2/5], loss: 0.000712
+Epoch [3/5], loss: 0.000714
+Epoch [4/5], loss: 0.000720
+Epoch [5/5], loss: 0.000708
+
+--- Training stage 5/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.000799
+Epoch [2/5], loss: 0.000718
+Epoch [3/5], loss: 0.000723
+Epoch [4/5], loss: 0.000713
+Epoch [5/5], loss: 0.000713
+
+[+] Training complete for sigma=15/255, gamma=2.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 30.53 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma15_gamma2_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma2p0_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma2p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..1b875cab38b5a53f3a3399581997f302c3e02779
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma2p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=15/255, gamma=2.0, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.002354
+Epoch [2/5], loss: 0.000895
+Epoch [3/5], loss: 0.000798
+Epoch [4/5], loss: 0.000753
+Epoch [5/5], loss: 0.000742
+
+--- Training stage 2/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001100
+Epoch [2/5], loss: 0.000763
+Epoch [3/5], loss: 0.000738
+Epoch [4/5], loss: 0.000733
+Epoch [5/5], loss: 0.000731
+
+--- Training stage 3/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.000978
+Epoch [2/5], loss: 0.000754
+Epoch [3/5], loss: 0.000726
+Epoch [4/5], loss: 0.000717
+Epoch [5/5], loss: 0.000724
+
+--- Training stage 4/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001047
+Epoch [2/5], loss: 0.000743
+Epoch [3/5], loss: 0.000733
+Epoch [4/5], loss: 0.000725
+Epoch [5/5], loss: 0.000713
+
+--- Training stage 5/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.000888
+Epoch [2/5], loss: 0.000740
+Epoch [3/5], loss: 0.000726
+Epoch [4/5], loss: 0.000714
+Epoch [5/5], loss: 0.000716
+
+[+] Training complete for sigma=15/255, gamma=2.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 30.53 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma15_gamma2_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma2p0_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma2p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..982fb8fe50984ee2c099a8dee963ebc25eb056bd
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma15_gamma2p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=15/255, gamma=2.0, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002626
+Epoch [2/5], loss: 0.000963
+Epoch [3/5], loss: 0.000804
+Epoch [4/5], loss: 0.000762
+Epoch [5/5], loss: 0.000744
+
+--- Training stage 2/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001537
+Epoch [2/5], loss: 0.000849
+Epoch [3/5], loss: 0.000766
+Epoch [4/5], loss: 0.000740
+Epoch [5/5], loss: 0.000733
+
+--- Training stage 3/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001347
+Epoch [2/5], loss: 0.000781
+Epoch [3/5], loss: 0.000744
+Epoch [4/5], loss: 0.000727
+Epoch [5/5], loss: 0.000729
+
+--- Training stage 4/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002017
+Epoch [2/5], loss: 0.000957
+Epoch [3/5], loss: 0.000823
+Epoch [4/5], loss: 0.000777
+Epoch [5/5], loss: 0.000761
+
+--- Training stage 5/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001921
+Epoch [2/5], loss: 0.000878
+Epoch [3/5], loss: 0.000774
+Epoch [4/5], loss: 0.000756
+Epoch [5/5], loss: 0.000742
+
+[+] Training complete for sigma=15/255, gamma=2.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 30.41 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma15_gamma2_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..052f3b0a2049b2fe221efd7a401f1c2ff15010e9
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma0p5_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=25/255, gamma=0.5, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003491
+Epoch [2/5], loss: 0.001645
+Epoch [3/5], loss: 0.001510
+Epoch [4/5], loss: 0.001459
+Epoch [5/5], loss: 0.001456
+
+--- Training stage 2/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001458
+Epoch [2/5], loss: 0.001388
+Epoch [3/5], loss: 0.001365
+Epoch [4/5], loss: 0.001371
+Epoch [5/5], loss: 0.001349
+
+--- Training stage 3/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001445
+Epoch [2/5], loss: 0.001370
+Epoch [3/5], loss: 0.001359
+Epoch [4/5], loss: 0.001354
+Epoch [5/5], loss: 0.001353
+
+--- Training stage 4/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001392
+Epoch [2/5], loss: 0.001369
+Epoch [3/5], loss: 0.001351
+Epoch [4/5], loss: 0.001369
+Epoch [5/5], loss: 0.001362
+
+--- Training stage 5/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001387
+Epoch [2/5], loss: 0.001380
+Epoch [3/5], loss: 0.001358
+Epoch [4/5], loss: 0.001341
+Epoch [5/5], loss: 0.001348
+
+[+] Training complete for sigma=25/255, gamma=0.5, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 27.89 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma25_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..e316de75ca63f8fa04113ab2a5c2867c7487a136
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma0p5_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=25/255, gamma=0.5, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003956
+Epoch [2/5], loss: 0.001698
+Epoch [3/5], loss: 0.001532
+Epoch [4/5], loss: 0.001475
+Epoch [5/5], loss: 0.001446
+
+--- Training stage 2/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001570
+Epoch [2/5], loss: 0.001397
+Epoch [3/5], loss: 0.001386
+Epoch [4/5], loss: 0.001378
+Epoch [5/5], loss: 0.001366
+
+--- Training stage 3/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001574
+Epoch [2/5], loss: 0.001390
+Epoch [3/5], loss: 0.001368
+Epoch [4/5], loss: 0.001364
+Epoch [5/5], loss: 0.001363
+
+--- Training stage 4/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001626
+Epoch [2/5], loss: 0.001377
+Epoch [3/5], loss: 0.001360
+Epoch [4/5], loss: 0.001372
+Epoch [5/5], loss: 0.001350
+
+--- Training stage 5/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001606
+Epoch [2/5], loss: 0.001384
+Epoch [3/5], loss: 0.001367
+Epoch [4/5], loss: 0.001356
+Epoch [5/5], loss: 0.001358
+
+[+] Training complete for sigma=25/255, gamma=0.5, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 27.86 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma25_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..ea4788e526a5baebd5ecf30476acece262d90ed8
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma0p5_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=25/255, gamma=0.5, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.006708
+Epoch [2/5], loss: 0.002225
+Epoch [3/5], loss: 0.001669
+Epoch [4/5], loss: 0.001546
+Epoch [5/5], loss: 0.001493
+
+--- Training stage 2/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002609
+Epoch [2/5], loss: 0.001624
+Epoch [3/5], loss: 0.001462
+Epoch [4/5], loss: 0.001403
+Epoch [5/5], loss: 0.001422
+
+--- Training stage 3/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002292
+Epoch [2/5], loss: 0.001529
+Epoch [3/5], loss: 0.001452
+Epoch [4/5], loss: 0.001412
+Epoch [5/5], loss: 0.001397
+
+--- Training stage 4/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002666
+Epoch [2/5], loss: 0.001513
+Epoch [3/5], loss: 0.001417
+Epoch [4/5], loss: 0.001396
+Epoch [5/5], loss: 0.001387
+
+--- Training stage 5/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001811
+Epoch [2/5], loss: 0.001435
+Epoch [3/5], loss: 0.001390
+Epoch [4/5], loss: 0.001395
+Epoch [5/5], loss: 0.001394
+
+[+] Training complete for sigma=25/255, gamma=0.5, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 27.79 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma25_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..25c1adc008bd6e661694e9235a334aafc177c943
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=25/255, gamma=1.0, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.004945
+Epoch [2/5], loss: 0.001903
+Epoch [3/5], loss: 0.001607
+Epoch [4/5], loss: 0.001532
+Epoch [5/5], loss: 0.001493
+
+--- Training stage 2/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001503
+Epoch [2/5], loss: 0.001390
+Epoch [3/5], loss: 0.001382
+Epoch [4/5], loss: 0.001400
+Epoch [5/5], loss: 0.001376
+
+--- Training stage 3/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001439
+Epoch [2/5], loss: 0.001359
+Epoch [3/5], loss: 0.001346
+Epoch [4/5], loss: 0.001349
+Epoch [5/5], loss: 0.001355
+
+--- Training stage 4/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001427
+Epoch [2/5], loss: 0.001364
+Epoch [3/5], loss: 0.001357
+Epoch [4/5], loss: 0.001352
+Epoch [5/5], loss: 0.001360
+
+--- Training stage 5/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001385
+Epoch [2/5], loss: 0.001363
+Epoch [3/5], loss: 0.001375
+Epoch [4/5], loss: 0.001361
+Epoch [5/5], loss: 0.001347
+
+[+] Training complete for sigma=25/255, gamma=1.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 27.85 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma25_gamma1_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..0aad3eb009d90d374b5f7a88c9e9d2bad68dbd16
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma1p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=25/255, gamma=1.0, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.014150
+Epoch [2/5], loss: 0.002495
+Epoch [3/5], loss: 0.001771
+Epoch [4/5], loss: 0.001620
+Epoch [5/5], loss: 0.001548
+
+--- Training stage 2/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001682
+Epoch [2/5], loss: 0.001451
+Epoch [3/5], loss: 0.001406
+Epoch [4/5], loss: 0.001406
+Epoch [5/5], loss: 0.001393
+
+--- Training stage 3/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001556
+Epoch [2/5], loss: 0.001413
+Epoch [3/5], loss: 0.001405
+Epoch [4/5], loss: 0.001386
+Epoch [5/5], loss: 0.001371
+
+--- Training stage 4/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001630
+Epoch [2/5], loss: 0.001443
+Epoch [3/5], loss: 0.001398
+Epoch [4/5], loss: 0.001408
+Epoch [5/5], loss: 0.001380
+
+--- Training stage 5/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001530
+Epoch [2/5], loss: 0.001409
+Epoch [3/5], loss: 0.001407
+Epoch [4/5], loss: 0.001397
+Epoch [5/5], loss: 0.001399
+
+[+] Training complete for sigma=25/255, gamma=1.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 27.76 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma25_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma1p0_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma1p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..7415e76d9646f48f889431ce7cb5adc7e5173986
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma1p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=25/255, gamma=1.0, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.004568
+Epoch [2/5], loss: 0.001733
+Epoch [3/5], loss: 0.001537
+Epoch [4/5], loss: 0.001486
+Epoch [5/5], loss: 0.001462
+
+--- Training stage 2/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002057
+Epoch [2/5], loss: 0.001475
+Epoch [3/5], loss: 0.001423
+Epoch [4/5], loss: 0.001396
+Epoch [5/5], loss: 0.001389
+
+--- Training stage 3/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001818
+Epoch [2/5], loss: 0.001457
+Epoch [3/5], loss: 0.001400
+Epoch [4/5], loss: 0.001404
+Epoch [5/5], loss: 0.001383
+
+--- Training stage 4/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002091
+Epoch [2/5], loss: 0.001451
+Epoch [3/5], loss: 0.001399
+Epoch [4/5], loss: 0.001380
+Epoch [5/5], loss: 0.001374
+
+--- Training stage 5/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002120
+Epoch [2/5], loss: 0.001491
+Epoch [3/5], loss: 0.001407
+Epoch [4/5], loss: 0.001399
+Epoch [5/5], loss: 0.001374
+
+[+] Training complete for sigma=25/255, gamma=1.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 27.82 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma25_gamma1_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma2p0_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma2p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..36bafb55a3ad151cd1bc30d0e1baa82e8859f4fc
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma2p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=25/255, gamma=2.0, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.005099
+Epoch [2/5], loss: 0.001765
+Epoch [3/5], loss: 0.001540
+Epoch [4/5], loss: 0.001492
+Epoch [5/5], loss: 0.001487
+
+--- Training stage 2/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001524
+Epoch [2/5], loss: 0.001427
+Epoch [3/5], loss: 0.001405
+Epoch [4/5], loss: 0.001368
+Epoch [5/5], loss: 0.001360
+
+--- Training stage 3/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001399
+Epoch [2/5], loss: 0.001382
+Epoch [3/5], loss: 0.001376
+Epoch [4/5], loss: 0.001356
+Epoch [5/5], loss: 0.001364
+
+--- Training stage 4/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001416
+Epoch [2/5], loss: 0.001352
+Epoch [3/5], loss: 0.001372
+Epoch [4/5], loss: 0.001356
+Epoch [5/5], loss: 0.001362
+
+--- Training stage 5/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001446
+Epoch [2/5], loss: 0.001371
+Epoch [3/5], loss: 0.001370
+Epoch [4/5], loss: 0.001374
+Epoch [5/5], loss: 0.001365
+
+[+] Training complete for sigma=25/255, gamma=2.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 27.87 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma25_gamma2_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma2p0_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma2p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..76c7d781a32d1878ae4446441f5c6a9e2039b24c
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma2p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=25/255, gamma=2.0, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.005221
+Epoch [2/5], loss: 0.001848
+Epoch [3/5], loss: 0.001600
+Epoch [4/5], loss: 0.001526
+Epoch [5/5], loss: 0.001470
+
+--- Training stage 2/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001814
+Epoch [2/5], loss: 0.001469
+Epoch [3/5], loss: 0.001442
+Epoch [4/5], loss: 0.001408
+Epoch [5/5], loss: 0.001371
+
+--- Training stage 3/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001584
+Epoch [2/5], loss: 0.001418
+Epoch [3/5], loss: 0.001403
+Epoch [4/5], loss: 0.001389
+Epoch [5/5], loss: 0.001371
+
+--- Training stage 4/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001732
+Epoch [2/5], loss: 0.001415
+Epoch [3/5], loss: 0.001380
+Epoch [4/5], loss: 0.001390
+Epoch [5/5], loss: 0.001358
+
+--- Training stage 5/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001546
+Epoch [2/5], loss: 0.001403
+Epoch [3/5], loss: 0.001371
+Epoch [4/5], loss: 0.001361
+Epoch [5/5], loss: 0.001361
+
+[+] Training complete for sigma=25/255, gamma=2.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 27.85 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma25_gamma2_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma2p0_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma2p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..6dffedefc3763538c20f151acf0258355a635eb9
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma25_gamma2p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=25/255, gamma=2.0, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.007437
+Epoch [2/5], loss: 0.002011
+Epoch [3/5], loss: 0.001623
+Epoch [4/5], loss: 0.001535
+Epoch [5/5], loss: 0.001489
+
+--- Training stage 2/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002781
+Epoch [2/5], loss: 0.001690
+Epoch [3/5], loss: 0.001509
+Epoch [4/5], loss: 0.001425
+Epoch [5/5], loss: 0.001404
+
+--- Training stage 3/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002095
+Epoch [2/5], loss: 0.001450
+Epoch [3/5], loss: 0.001399
+Epoch [4/5], loss: 0.001399
+Epoch [5/5], loss: 0.001377
+
+--- Training stage 4/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001954
+Epoch [2/5], loss: 0.001451
+Epoch [3/5], loss: 0.001414
+Epoch [4/5], loss: 0.001390
+Epoch [5/5], loss: 0.001378
+
+--- Training stage 5/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002298
+Epoch [2/5], loss: 0.001527
+Epoch [3/5], loss: 0.001443
+Epoch [4/5], loss: 0.001414
+Epoch [5/5], loss: 0.001397
+
+[+] Training complete for sigma=25/255, gamma=2.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 27.78 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma25_gamma2_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..8a13ff79cab4c9a6d4b380c9244f848b50151263
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma0p5_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=50/255, gamma=0.5, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.015735
+Epoch [2/5], loss: 0.004754
+Epoch [3/5], loss: 0.004196
+Epoch [4/5], loss: 0.003981
+Epoch [5/5], loss: 0.003845
+
+--- Training stage 2/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003722
+Epoch [2/5], loss: 0.003397
+Epoch [3/5], loss: 0.003331
+Epoch [4/5], loss: 0.003352
+Epoch [5/5], loss: 0.003320
+
+--- Training stage 3/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003319
+Epoch [2/5], loss: 0.003313
+Epoch [3/5], loss: 0.003305
+Epoch [4/5], loss: 0.003327
+Epoch [5/5], loss: 0.003329
+
+--- Training stage 4/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003297
+Epoch [2/5], loss: 0.003297
+Epoch [3/5], loss: 0.003269
+Epoch [4/5], loss: 0.003312
+Epoch [5/5], loss: 0.003274
+
+--- Training stage 5/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003342
+Epoch [2/5], loss: 0.003311
+Epoch [3/5], loss: 0.003289
+Epoch [4/5], loss: 0.003311
+Epoch [5/5], loss: 0.003326
+
+[+] Training complete for sigma=50/255, gamma=0.5, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 24.47 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma50_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..df339da59e2284fd943798b6effbe64e4e535b2d
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma0p5_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=50/255, gamma=0.5, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.015470
+Epoch [2/5], loss: 0.004883
+Epoch [3/5], loss: 0.004100
+Epoch [4/5], loss: 0.003946
+Epoch [5/5], loss: 0.003829
+
+--- Training stage 2/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003800
+Epoch [2/5], loss: 0.003445
+Epoch [3/5], loss: 0.003400
+Epoch [4/5], loss: 0.003361
+Epoch [5/5], loss: 0.003334
+
+--- Training stage 3/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003596
+Epoch [2/5], loss: 0.003309
+Epoch [3/5], loss: 0.003340
+Epoch [4/5], loss: 0.003334
+Epoch [5/5], loss: 0.003284
+
+--- Training stage 4/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003511
+Epoch [2/5], loss: 0.003326
+Epoch [3/5], loss: 0.003329
+Epoch [4/5], loss: 0.003298
+Epoch [5/5], loss: 0.003299
+
+--- Training stage 5/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003401
+Epoch [2/5], loss: 0.003309
+Epoch [3/5], loss: 0.003317
+Epoch [4/5], loss: 0.003345
+Epoch [5/5], loss: 0.003259
+
+[+] Training complete for sigma=50/255, gamma=0.5, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 24.45 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma50_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..903d4bf2b830062f21ee431379edbcf17849d379
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma0p5_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=50/255, gamma=0.5, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.013599
+Epoch [2/5], loss: 0.004781
+Epoch [3/5], loss: 0.004091
+Epoch [4/5], loss: 0.003886
+Epoch [5/5], loss: 0.003816
+
+--- Training stage 2/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.004003
+Epoch [2/5], loss: 0.003446
+Epoch [3/5], loss: 0.003392
+Epoch [4/5], loss: 0.003336
+Epoch [5/5], loss: 0.003355
+
+--- Training stage 3/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.003927
+Epoch [2/5], loss: 0.003391
+Epoch [3/5], loss: 0.003348
+Epoch [4/5], loss: 0.003361
+Epoch [5/5], loss: 0.003300
+
+--- Training stage 4/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.003702
+Epoch [2/5], loss: 0.003404
+Epoch [3/5], loss: 0.003373
+Epoch [4/5], loss: 0.003340
+Epoch [5/5], loss: 0.003254
+
+--- Training stage 5/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.003997
+Epoch [2/5], loss: 0.003408
+Epoch [3/5], loss: 0.003345
+Epoch [4/5], loss: 0.003301
+Epoch [5/5], loss: 0.003333
+
+[+] Training complete for sigma=50/255, gamma=0.5, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 24.43 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma50_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..6767a421e25232b1a72e1876b34b933a901878eb
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=50/255, gamma=1.0, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.013472
+Epoch [2/5], loss: 0.004621
+Epoch [3/5], loss: 0.004023
+Epoch [4/5], loss: 0.003838
+Epoch [5/5], loss: 0.003734
+
+--- Training stage 2/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003769
+Epoch [2/5], loss: 0.003448
+Epoch [3/5], loss: 0.003379
+Epoch [4/5], loss: 0.003399
+Epoch [5/5], loss: 0.003372
+
+--- Training stage 3/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003330
+Epoch [2/5], loss: 0.003360
+Epoch [3/5], loss: 0.003348
+Epoch [4/5], loss: 0.003370
+Epoch [5/5], loss: 0.003316
+
+--- Training stage 4/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003351
+Epoch [2/5], loss: 0.003289
+Epoch [3/5], loss: 0.003348
+Epoch [4/5], loss: 0.003253
+Epoch [5/5], loss: 0.003303
+
+--- Training stage 5/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003310
+Epoch [2/5], loss: 0.003306
+Epoch [3/5], loss: 0.003247
+Epoch [4/5], loss: 0.003282
+Epoch [5/5], loss: 0.003293
+
+[+] Training complete for sigma=50/255, gamma=1.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 24.47 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma50_gamma1_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..9e00feaf9be25bb19c4ec57eb2608fcdb6c85731
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma1p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=50/255, gamma=1.0, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.010699
+Epoch [2/5], loss: 0.004451
+Epoch [3/5], loss: 0.004036
+Epoch [4/5], loss: 0.003857
+Epoch [5/5], loss: 0.003774
+
+--- Training stage 2/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003838
+Epoch [2/5], loss: 0.003474
+Epoch [3/5], loss: 0.003376
+Epoch [4/5], loss: 0.003383
+Epoch [5/5], loss: 0.003319
+
+--- Training stage 3/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003491
+Epoch [2/5], loss: 0.003320
+Epoch [3/5], loss: 0.003315
+Epoch [4/5], loss: 0.003273
+Epoch [5/5], loss: 0.003281
+
+--- Training stage 4/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003523
+Epoch [2/5], loss: 0.003313
+Epoch [3/5], loss: 0.003317
+Epoch [4/5], loss: 0.003305
+Epoch [5/5], loss: 0.003288
+
+--- Training stage 5/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003513
+Epoch [2/5], loss: 0.003291
+Epoch [3/5], loss: 0.003315
+Epoch [4/5], loss: 0.003285
+Epoch [5/5], loss: 0.003266
+
+[+] Training complete for sigma=50/255, gamma=1.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 24.45 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma50_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma1p0_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma1p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..a732be10ea0ceccf0abc63929ce8128d7fd3229e
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma1p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=50/255, gamma=1.0, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.012162
+Epoch [2/5], loss: 0.004666
+Epoch [3/5], loss: 0.004031
+Epoch [4/5], loss: 0.003873
+Epoch [5/5], loss: 0.003799
+
+--- Training stage 2/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.004541
+Epoch [2/5], loss: 0.003557
+Epoch [3/5], loss: 0.003414
+Epoch [4/5], loss: 0.003369
+Epoch [5/5], loss: 0.003368
+
+--- Training stage 3/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.005007
+Epoch [2/5], loss: 0.003559
+Epoch [3/5], loss: 0.003380
+Epoch [4/5], loss: 0.003348
+Epoch [5/5], loss: 0.003306
+
+--- Training stage 4/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.004062
+Epoch [2/5], loss: 0.003385
+Epoch [3/5], loss: 0.003388
+Epoch [4/5], loss: 0.003329
+Epoch [5/5], loss: 0.003352
+
+--- Training stage 5/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.004325
+Epoch [2/5], loss: 0.003421
+Epoch [3/5], loss: 0.003349
+Epoch [4/5], loss: 0.003345
+Epoch [5/5], loss: 0.003285
+
+[+] Training complete for sigma=50/255, gamma=1.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 24.44 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma50_gamma1_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma2p0_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma2p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..1dcc3f1ad8baf9325b0b45b0ca3ea9aa124c7a34
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma2p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=50/255, gamma=2.0, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.012681
+Epoch [2/5], loss: 0.004632
+Epoch [3/5], loss: 0.004064
+Epoch [4/5], loss: 0.003868
+Epoch [5/5], loss: 0.003744
+
+--- Training stage 2/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003622
+Epoch [2/5], loss: 0.003437
+Epoch [3/5], loss: 0.003353
+Epoch [4/5], loss: 0.003329
+Epoch [5/5], loss: 0.003359
+
+--- Training stage 3/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003370
+Epoch [2/5], loss: 0.003292
+Epoch [3/5], loss: 0.003342
+Epoch [4/5], loss: 0.003270
+Epoch [5/5], loss: 0.003308
+
+--- Training stage 4/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003360
+Epoch [2/5], loss: 0.003279
+Epoch [3/5], loss: 0.003240
+Epoch [4/5], loss: 0.003258
+Epoch [5/5], loss: 0.003326
+
+--- Training stage 5/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003310
+Epoch [2/5], loss: 0.003308
+Epoch [3/5], loss: 0.003299
+Epoch [4/5], loss: 0.003286
+Epoch [5/5], loss: 0.003275
+
+[+] Training complete for sigma=50/255, gamma=2.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 24.48 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma50_gamma2_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma2p0_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma2p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..b79d6365df1f50e1df3c3b87bc5ae677c470978a
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma2p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=50/255, gamma=2.0, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.008190
+Epoch [2/5], loss: 0.004173
+Epoch [3/5], loss: 0.003859
+Epoch [4/5], loss: 0.003803
+Epoch [5/5], loss: 0.003740
+
+--- Training stage 2/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003889
+Epoch [2/5], loss: 0.003386
+Epoch [3/5], loss: 0.003353
+Epoch [4/5], loss: 0.003369
+Epoch [5/5], loss: 0.003355
+
+--- Training stage 3/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003747
+Epoch [2/5], loss: 0.003385
+Epoch [3/5], loss: 0.003334
+Epoch [4/5], loss: 0.003263
+Epoch [5/5], loss: 0.003281
+
+--- Training stage 4/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003526
+Epoch [2/5], loss: 0.003370
+Epoch [3/5], loss: 0.003287
+Epoch [4/5], loss: 0.003296
+Epoch [5/5], loss: 0.003286
+
+--- Training stage 5/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003540
+Epoch [2/5], loss: 0.003307
+Epoch [3/5], loss: 0.003242
+Epoch [4/5], loss: 0.003271
+Epoch [5/5], loss: 0.003306
+
+[+] Training complete for sigma=50/255, gamma=2.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 24.46 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma50_gamma2_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma2p0_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma2p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..7d180a8592d13e6eefc30077bcf44a6725b7b8ae
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma50_gamma2p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=50/255, gamma=2.0, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.012516
+Epoch [2/5], loss: 0.005025
+Epoch [3/5], loss: 0.004154
+Epoch [4/5], loss: 0.003868
+Epoch [5/5], loss: 0.003829
+
+--- Training stage 2/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.004362
+Epoch [2/5], loss: 0.003449
+Epoch [3/5], loss: 0.003423
+Epoch [4/5], loss: 0.003337
+Epoch [5/5], loss: 0.003382
+
+--- Training stage 3/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.004138
+Epoch [2/5], loss: 0.003412
+Epoch [3/5], loss: 0.003375
+Epoch [4/5], loss: 0.003326
+Epoch [5/5], loss: 0.003302
+
+--- Training stage 4/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.003907
+Epoch [2/5], loss: 0.003409
+Epoch [3/5], loss: 0.003370
+Epoch [4/5], loss: 0.003344
+Epoch [5/5], loss: 0.003336
+
+--- Training stage 5/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.004257
+Epoch [2/5], loss: 0.003378
+Epoch [3/5], loss: 0.003318
+Epoch [4/5], loss: 0.003325
+Epoch [5/5], loss: 0.003347
+
+[+] Training complete for sigma=50/255, gamma=2.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 24.43 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma50_gamma2_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..dc0d1eec1b8464a2b610c8d3135e19f9c51c310d
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma0p5_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=75/255, gamma=0.5, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.032973
+Epoch [2/5], loss: 0.009030
+Epoch [3/5], loss: 0.007617
+Epoch [4/5], loss: 0.007185
+Epoch [5/5], loss: 0.006990
+
+--- Training stage 2/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.006659
+Epoch [2/5], loss: 0.006316
+Epoch [3/5], loss: 0.006198
+Epoch [4/5], loss: 0.006135
+Epoch [5/5], loss: 0.006092
+
+--- Training stage 3/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.006117
+Epoch [2/5], loss: 0.006034
+Epoch [3/5], loss: 0.006022
+Epoch [4/5], loss: 0.005966
+Epoch [5/5], loss: 0.005944
+
+--- Training stage 4/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.006016
+Epoch [2/5], loss: 0.006050
+Epoch [3/5], loss: 0.005910
+Epoch [4/5], loss: 0.006009
+Epoch [5/5], loss: 0.005987
+
+--- Training stage 5/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.005999
+Epoch [2/5], loss: 0.005940
+Epoch [3/5], loss: 0.005907
+Epoch [4/5], loss: 0.005971
+Epoch [5/5], loss: 0.005937
+
+[+] Training complete for sigma=75/255, gamma=0.5, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 22.29 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma75_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..5d299413da0eac7f8dccbd89bc7250f1a14c760c
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma0p5_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=75/255, gamma=0.5, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.023829
+Epoch [2/5], loss: 0.009203
+Epoch [3/5], loss: 0.007753
+Epoch [4/5], loss: 0.007278
+Epoch [5/5], loss: 0.007092
+
+--- Training stage 2/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006767
+Epoch [2/5], loss: 0.006236
+Epoch [3/5], loss: 0.006144
+Epoch [4/5], loss: 0.006084
+Epoch [5/5], loss: 0.006017
+
+--- Training stage 3/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006263
+Epoch [2/5], loss: 0.005981
+Epoch [3/5], loss: 0.006013
+Epoch [4/5], loss: 0.006047
+Epoch [5/5], loss: 0.005949
+
+--- Training stage 4/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006071
+Epoch [2/5], loss: 0.005982
+Epoch [3/5], loss: 0.005972
+Epoch [4/5], loss: 0.005979
+Epoch [5/5], loss: 0.005920
+
+--- Training stage 5/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006054
+Epoch [2/5], loss: 0.006001
+Epoch [3/5], loss: 0.005897
+Epoch [4/5], loss: 0.005964
+Epoch [5/5], loss: 0.005988
+
+[+] Training complete for sigma=75/255, gamma=0.5, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 22.30 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma75_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..e7e534b8daddd0b86db5966899f7ee4a9568a794
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma0p5_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=75/255, gamma=0.5, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.025228
+Epoch [2/5], loss: 0.009210
+Epoch [3/5], loss: 0.007821
+Epoch [4/5], loss: 0.007447
+Epoch [5/5], loss: 0.007193
+
+--- Training stage 2/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.007715
+Epoch [2/5], loss: 0.006351
+Epoch [3/5], loss: 0.006198
+Epoch [4/5], loss: 0.006149
+Epoch [5/5], loss: 0.006149
+
+--- Training stage 3/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.006776
+Epoch [2/5], loss: 0.006190
+Epoch [3/5], loss: 0.005952
+Epoch [4/5], loss: 0.005980
+Epoch [5/5], loss: 0.006013
+
+--- Training stage 4/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.006539
+Epoch [2/5], loss: 0.006045
+Epoch [3/5], loss: 0.006017
+Epoch [4/5], loss: 0.006000
+Epoch [5/5], loss: 0.005996
+
+--- Training stage 5/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.006634
+Epoch [2/5], loss: 0.006054
+Epoch [3/5], loss: 0.005906
+Epoch [4/5], loss: 0.005962
+Epoch [5/5], loss: 0.005907
+
+[+] Training complete for sigma=75/255, gamma=0.5, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 22.29 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma75_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..0848bd901f1bc0efc190bdfe3b55084de78c7b43
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=75/255, gamma=1.0, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.031908
+Epoch [2/5], loss: 0.009166
+Epoch [3/5], loss: 0.007713
+Epoch [4/5], loss: 0.007299
+Epoch [5/5], loss: 0.007045
+
+--- Training stage 2/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.006567
+Epoch [2/5], loss: 0.006162
+Epoch [3/5], loss: 0.006106
+Epoch [4/5], loss: 0.006156
+Epoch [5/5], loss: 0.006194
+
+--- Training stage 3/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.006105
+Epoch [2/5], loss: 0.005992
+Epoch [3/5], loss: 0.006002
+Epoch [4/5], loss: 0.006011
+Epoch [5/5], loss: 0.006029
+
+--- Training stage 4/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.005988
+Epoch [2/5], loss: 0.005943
+Epoch [3/5], loss: 0.005958
+Epoch [4/5], loss: 0.005939
+Epoch [5/5], loss: 0.005935
+
+--- Training stage 5/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.005999
+Epoch [2/5], loss: 0.005942
+Epoch [3/5], loss: 0.005971
+Epoch [4/5], loss: 0.005977
+Epoch [5/5], loss: 0.005957
+
+[+] Training complete for sigma=75/255, gamma=1.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 22.30 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma75_gamma1_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..7cbe1c4c1a1bfe98f1fa8e4dab3e803182f55368
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma1p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=75/255, gamma=1.0, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.027874
+Epoch [2/5], loss: 0.009239
+Epoch [3/5], loss: 0.007819
+Epoch [4/5], loss: 0.007286
+Epoch [5/5], loss: 0.007103
+
+--- Training stage 2/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006423
+Epoch [2/5], loss: 0.006062
+Epoch [3/5], loss: 0.006126
+Epoch [4/5], loss: 0.006100
+Epoch [5/5], loss: 0.006105
+
+--- Training stage 3/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006273
+Epoch [2/5], loss: 0.006055
+Epoch [3/5], loss: 0.006018
+Epoch [4/5], loss: 0.006032
+Epoch [5/5], loss: 0.006025
+
+--- Training stage 4/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006076
+Epoch [2/5], loss: 0.005964
+Epoch [3/5], loss: 0.005990
+Epoch [4/5], loss: 0.005866
+Epoch [5/5], loss: 0.005970
+
+--- Training stage 5/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.005992
+Epoch [2/5], loss: 0.005925
+Epoch [3/5], loss: 0.006013
+Epoch [4/5], loss: 0.006010
+Epoch [5/5], loss: 0.005963
+
+[+] Training complete for sigma=75/255, gamma=1.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 22.28 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma75_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma1p0_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma1p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..26606b4388a512bf27fec34b7b9bb7a5bfda618b
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma1p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=75/255, gamma=1.0, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.025801
+Epoch [2/5], loss: 0.009541
+Epoch [3/5], loss: 0.008064
+Epoch [4/5], loss: 0.007482
+Epoch [5/5], loss: 0.007187
+
+--- Training stage 2/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.007633
+Epoch [2/5], loss: 0.006442
+Epoch [3/5], loss: 0.006147
+Epoch [4/5], loss: 0.006117
+Epoch [5/5], loss: 0.006080
+
+--- Training stage 3/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.006683
+Epoch [2/5], loss: 0.006038
+Epoch [3/5], loss: 0.006053
+Epoch [4/5], loss: 0.005950
+Epoch [5/5], loss: 0.005944
+
+--- Training stage 4/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.006629
+Epoch [2/5], loss: 0.006039
+Epoch [3/5], loss: 0.006007
+Epoch [4/5], loss: 0.005977
+Epoch [5/5], loss: 0.005947
+
+--- Training stage 5/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.006379
+Epoch [2/5], loss: 0.006020
+Epoch [3/5], loss: 0.005987
+Epoch [4/5], loss: 0.005983
+Epoch [5/5], loss: 0.005953
+
+[+] Training complete for sigma=75/255, gamma=1.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 22.29 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma75_gamma1_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma2p0_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma2p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..8d69994092c7eb107ebb36be2ae97074fa6b4a81
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma2p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=75/255, gamma=2.0, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.028901
+Epoch [2/5], loss: 0.009121
+Epoch [3/5], loss: 0.007720
+Epoch [4/5], loss: 0.007263
+Epoch [5/5], loss: 0.007047
+
+--- Training stage 2/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.006399
+Epoch [2/5], loss: 0.006100
+Epoch [3/5], loss: 0.006120
+Epoch [4/5], loss: 0.006167
+Epoch [5/5], loss: 0.006179
+
+--- Training stage 3/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.006171
+Epoch [2/5], loss: 0.006049
+Epoch [3/5], loss: 0.006053
+Epoch [4/5], loss: 0.005967
+Epoch [5/5], loss: 0.005933
+
+--- Training stage 4/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.005966
+Epoch [2/5], loss: 0.006011
+Epoch [3/5], loss: 0.005940
+Epoch [4/5], loss: 0.005913
+Epoch [5/5], loss: 0.005933
+
+--- Training stage 5/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.005904
+Epoch [2/5], loss: 0.005954
+Epoch [3/5], loss: 0.005936
+Epoch [4/5], loss: 0.005892
+Epoch [5/5], loss: 0.005903
+
+[+] Training complete for sigma=75/255, gamma=2.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 22.31 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma75_gamma2_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma2p0_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma2p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..8a4cb10c0150a41632f2b8aa6c4b22852acfb983
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma2p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=75/255, gamma=2.0, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.016099
+Epoch [2/5], loss: 0.007794
+Epoch [3/5], loss: 0.007241
+Epoch [4/5], loss: 0.007013
+Epoch [5/5], loss: 0.006878
+
+--- Training stage 2/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006426
+Epoch [2/5], loss: 0.006174
+Epoch [3/5], loss: 0.006055
+Epoch [4/5], loss: 0.006077
+Epoch [5/5], loss: 0.006056
+
+--- Training stage 3/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006218
+Epoch [2/5], loss: 0.006023
+Epoch [3/5], loss: 0.005988
+Epoch [4/5], loss: 0.005993
+Epoch [5/5], loss: 0.005970
+
+--- Training stage 4/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006101
+Epoch [2/5], loss: 0.005977
+Epoch [3/5], loss: 0.005967
+Epoch [4/5], loss: 0.005990
+Epoch [5/5], loss: 0.005989
+
+--- Training stage 5/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006065
+Epoch [2/5], loss: 0.005963
+Epoch [3/5], loss: 0.005968
+Epoch [4/5], loss: 0.006004
+Epoch [5/5], loss: 0.005905
+
+[+] Training complete for sigma=75/255, gamma=2.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 22.31 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma75_gamma2_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma2p0_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma2p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..71e0600873046ffbbc94da93126189c0b98116b8
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_nowave_sigma75_gamma2p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage TNRD model with RBF activation for sigma=75/255, gamma=2.0, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.028367
+Epoch [2/5], loss: 0.009630
+Epoch [3/5], loss: 0.007900
+Epoch [4/5], loss: 0.007438
+Epoch [5/5], loss: 0.007255
+
+--- Training stage 2/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.007605
+Epoch [2/5], loss: 0.006336
+Epoch [3/5], loss: 0.006203
+Epoch [4/5], loss: 0.006205
+Epoch [5/5], loss: 0.006143
+
+--- Training stage 3/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.006952
+Epoch [2/5], loss: 0.006042
+Epoch [3/5], loss: 0.006025
+Epoch [4/5], loss: 0.006047
+Epoch [5/5], loss: 0.006057
+
+--- Training stage 4/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.006779
+Epoch [2/5], loss: 0.006020
+Epoch [3/5], loss: 0.005955
+Epoch [4/5], loss: 0.005970
+Epoch [5/5], loss: 0.005963
+
+--- Training stage 5/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.006448
+Epoch [2/5], loss: 0.006040
+Epoch [3/5], loss: 0.005981
+Epoch [4/5], loss: 0.005979
+Epoch [5/5], loss: 0.005959
+
+[+] Training complete for sigma=75/255, gamma=2.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 22.28 dB
+[+] Saved checkpoint: rbf_model_5stages_waveFalse_sigma75_gamma2_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..9af1f66c5f3dc11271066f0c96029a0ad5d9df40
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma0p5_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=15/255, gamma=0.5, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003004
+Epoch [2/5], loss: 0.002178
+Epoch [3/5], loss: 0.001265
+Epoch [4/5], loss: 0.000876
+Epoch [5/5], loss: 0.000804
+
+--- Training stage 2/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003001
+Epoch [2/5], loss: 0.002768
+Epoch [3/5], loss: 0.002375
+Epoch [4/5], loss: 0.001983
+Epoch [5/5], loss: 0.001722
+
+--- Training stage 3/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.004491
+Epoch [2/5], loss: 0.003823
+Epoch [3/5], loss: 0.002782
+Epoch [4/5], loss: 0.001773
+Epoch [5/5], loss: 0.001366
+
+--- Training stage 4/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.002422
+Epoch [2/5], loss: 0.002407
+Epoch [3/5], loss: 0.002306
+Epoch [4/5], loss: 0.002229
+Epoch [5/5], loss: 0.002208
+
+--- Training stage 5/5 for sigma=15/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.004332
+Epoch [2/5], loss: 0.003936
+Epoch [3/5], loss: 0.003334
+Epoch [4/5], loss: 0.002635
+Epoch [5/5], loss: 0.002303
+
+[+] Training complete for sigma=15/255, gamma=0.5, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 25.79 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma15_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..316461574865f519243d1d12a69c6f0bb4b524e5
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma0p5_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=15/255, gamma=0.5, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.002623
+Epoch [2/5], loss: 0.001206
+Epoch [3/5], loss: 0.000835
+Epoch [4/5], loss: 0.000780
+Epoch [5/5], loss: 0.000761
+
+--- Training stage 2/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.002873
+Epoch [2/5], loss: 0.002385
+Epoch [3/5], loss: 0.002014
+Epoch [4/5], loss: 0.001860
+Epoch [5/5], loss: 0.001800
+
+--- Training stage 3/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.004041
+Epoch [2/5], loss: 0.002354
+Epoch [3/5], loss: 0.001354
+Epoch [4/5], loss: 0.001162
+Epoch [5/5], loss: 0.001115
+
+--- Training stage 4/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.002400
+Epoch [2/5], loss: 0.002333
+Epoch [3/5], loss: 0.002235
+Epoch [4/5], loss: 0.002187
+Epoch [5/5], loss: 0.002133
+
+--- Training stage 5/5 for sigma=15/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003960
+Epoch [2/5], loss: 0.002785
+Epoch [3/5], loss: 0.002110
+Epoch [4/5], loss: 0.001966
+Epoch [5/5], loss: 0.001934
+
+[+] Training complete for sigma=15/255, gamma=0.5, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 26.43 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma15_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..0241e169aacdd10addd68b4a3a858d9f1124ba52
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma0p5_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=15/255, gamma=0.5, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001782
+Epoch [2/5], loss: 0.000872
+Epoch [3/5], loss: 0.000793
+Epoch [4/5], loss: 0.000744
+Epoch [5/5], loss: 0.000734
+
+--- Training stage 2/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002445
+Epoch [2/5], loss: 0.002056
+Epoch [3/5], loss: 0.001946
+Epoch [4/5], loss: 0.001900
+Epoch [5/5], loss: 0.001875
+
+--- Training stage 3/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.003269
+Epoch [2/5], loss: 0.001449
+Epoch [3/5], loss: 0.001208
+Epoch [4/5], loss: 0.001095
+Epoch [5/5], loss: 0.001075
+
+--- Training stage 4/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002303
+Epoch [2/5], loss: 0.002171
+Epoch [3/5], loss: 0.002094
+Epoch [4/5], loss: 0.002051
+Epoch [5/5], loss: 0.001985
+
+--- Training stage 5/5 for sigma=15/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002811
+Epoch [2/5], loss: 0.001903
+Epoch [3/5], loss: 0.001767
+Epoch [4/5], loss: 0.001723
+Epoch [5/5], loss: 0.001711
+
+[+] Training complete for sigma=15/255, gamma=0.5, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 26.95 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma15_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..871660dc98153fac98bb95f4569eabaa745c7e71
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=15/255, gamma=1.0, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003102
+Epoch [2/5], loss: 0.002328
+Epoch [3/5], loss: 0.001375
+Epoch [4/5], loss: 0.000893
+Epoch [5/5], loss: 0.000805
+
+--- Training stage 2/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.002918
+Epoch [2/5], loss: 0.002686
+Epoch [3/5], loss: 0.002335
+Epoch [4/5], loss: 0.001962
+Epoch [5/5], loss: 0.001709
+
+--- Training stage 3/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.004241
+Epoch [2/5], loss: 0.003626
+Epoch [3/5], loss: 0.002678
+Epoch [4/5], loss: 0.001757
+Epoch [5/5], loss: 0.001324
+
+--- Training stage 4/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.002254
+Epoch [2/5], loss: 0.002176
+Epoch [3/5], loss: 0.002128
+Epoch [4/5], loss: 0.002084
+Epoch [5/5], loss: 0.002034
+
+--- Training stage 5/5 for sigma=15/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003780
+Epoch [2/5], loss: 0.003443
+Epoch [3/5], loss: 0.002946
+Epoch [4/5], loss: 0.002471
+Epoch [5/5], loss: 0.002116
+
+[+] Training complete for sigma=15/255, gamma=1.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 26.14 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma15_gamma1_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..1e76c812422daa821f8f2123f682c9a179b84a52
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma1p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=15/255, gamma=1.0, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.002387
+Epoch [2/5], loss: 0.001151
+Epoch [3/5], loss: 0.000833
+Epoch [4/5], loss: 0.000778
+Epoch [5/5], loss: 0.000759
+
+--- Training stage 2/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.002692
+Epoch [2/5], loss: 0.002251
+Epoch [3/5], loss: 0.001908
+Epoch [4/5], loss: 0.001745
+Epoch [5/5], loss: 0.001679
+
+--- Training stage 3/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003484
+Epoch [2/5], loss: 0.002119
+Epoch [3/5], loss: 0.001295
+Epoch [4/5], loss: 0.001113
+Epoch [5/5], loss: 0.001080
+
+--- Training stage 4/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.002113
+Epoch [2/5], loss: 0.002084
+Epoch [3/5], loss: 0.002011
+Epoch [4/5], loss: 0.001958
+Epoch [5/5], loss: 0.001898
+
+--- Training stage 5/5 for sigma=15/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003325
+Epoch [2/5], loss: 0.002472
+Epoch [3/5], loss: 0.001907
+Epoch [4/5], loss: 0.001751
+Epoch [5/5], loss: 0.001732
+
+[+] Training complete for sigma=15/255, gamma=1.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 26.85 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma15_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma1p0_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma1p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..603f1d0cb53b87e01b33fe437b429b94a495af6f
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma1p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=15/255, gamma=1.0, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001434
+Epoch [2/5], loss: 0.000811
+Epoch [3/5], loss: 0.000754
+Epoch [4/5], loss: 0.000733
+Epoch [5/5], loss: 0.000720
+
+--- Training stage 2/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002171
+Epoch [2/5], loss: 0.001907
+Epoch [3/5], loss: 0.001810
+Epoch [4/5], loss: 0.001764
+Epoch [5/5], loss: 0.001717
+
+--- Training stage 3/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002949
+Epoch [2/5], loss: 0.001366
+Epoch [3/5], loss: 0.001128
+Epoch [4/5], loss: 0.001066
+Epoch [5/5], loss: 0.001030
+
+--- Training stage 4/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001864
+Epoch [2/5], loss: 0.001762
+Epoch [3/5], loss: 0.001719
+Epoch [4/5], loss: 0.001654
+Epoch [5/5], loss: 0.001620
+
+--- Training stage 5/5 for sigma=15/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.002408
+Epoch [2/5], loss: 0.001608
+Epoch [3/5], loss: 0.001508
+Epoch [4/5], loss: 0.001440
+Epoch [5/5], loss: 0.001433
+
+[+] Training complete for sigma=15/255, gamma=1.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 27.58 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma15_gamma1_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma2p0_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma2p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..25bd8649a63c7d98376c7bd93e823aa8ff07d24d
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma2p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=15/255, gamma=2.0, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003008
+Epoch [2/5], loss: 0.002238
+Epoch [3/5], loss: 0.001352
+Epoch [4/5], loss: 0.000908
+Epoch [5/5], loss: 0.000808
+
+--- Training stage 2/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.002668
+Epoch [2/5], loss: 0.002427
+Epoch [3/5], loss: 0.002125
+Epoch [4/5], loss: 0.001769
+Epoch [5/5], loss: 0.001508
+
+--- Training stage 3/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003366
+Epoch [2/5], loss: 0.002926
+Epoch [3/5], loss: 0.002297
+Epoch [4/5], loss: 0.001673
+Epoch [5/5], loss: 0.001329
+
+--- Training stage 4/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001923
+Epoch [2/5], loss: 0.001863
+Epoch [3/5], loss: 0.001853
+Epoch [4/5], loss: 0.001798
+Epoch [5/5], loss: 0.001744
+
+--- Training stage 5/5 for sigma=15/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.002736
+Epoch [2/5], loss: 0.002632
+Epoch [3/5], loss: 0.002394
+Epoch [4/5], loss: 0.002109
+Epoch [5/5], loss: 0.001945
+
+[+] Training complete for sigma=15/255, gamma=2.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 26.49 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma15_gamma2_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma2p0_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma2p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..f7e48fdb8a9bc59c0a782eb3b0b6d0438569b1a5
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma2p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=15/255, gamma=2.0, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.002589
+Epoch [2/5], loss: 0.001259
+Epoch [3/5], loss: 0.000849
+Epoch [4/5], loss: 0.000787
+Epoch [5/5], loss: 0.000762
+
+--- Training stage 2/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.002302
+Epoch [2/5], loss: 0.001917
+Epoch [3/5], loss: 0.001647
+Epoch [4/5], loss: 0.001526
+Epoch [5/5], loss: 0.001448
+
+--- Training stage 3/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.002821
+Epoch [2/5], loss: 0.001859
+Epoch [3/5], loss: 0.001231
+Epoch [4/5], loss: 0.001037
+Epoch [5/5], loss: 0.001033
+
+--- Training stage 4/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001673
+Epoch [2/5], loss: 0.001616
+Epoch [3/5], loss: 0.001591
+Epoch [4/5], loss: 0.001515
+Epoch [5/5], loss: 0.001479
+
+--- Training stage 5/5 for sigma=15/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.002180
+Epoch [2/5], loss: 0.001832
+Epoch [3/5], loss: 0.001545
+Epoch [4/5], loss: 0.001425
+Epoch [5/5], loss: 0.001411
+
+[+] Training complete for sigma=15/255, gamma=2.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 27.56 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma15_gamma2_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma2p0_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma2p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..e11f7026e5085f5af8946986965e7dea60f03c0c
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma15_gamma2p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=15/255, gamma=2.0, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001639
+Epoch [2/5], loss: 0.000864
+Epoch [3/5], loss: 0.000777
+Epoch [4/5], loss: 0.000752
+Epoch [5/5], loss: 0.000736
+
+--- Training stage 2/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001793
+Epoch [2/5], loss: 0.001537
+Epoch [3/5], loss: 0.001437
+Epoch [4/5], loss: 0.001380
+Epoch [5/5], loss: 0.001342
+
+--- Training stage 3/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001946
+Epoch [2/5], loss: 0.001122
+Epoch [3/5], loss: 0.000971
+Epoch [4/5], loss: 0.000945
+Epoch [5/5], loss: 0.000923
+
+--- Training stage 4/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001365
+Epoch [2/5], loss: 0.001296
+Epoch [3/5], loss: 0.001235
+Epoch [4/5], loss: 0.001207
+Epoch [5/5], loss: 0.001188
+
+--- Training stage 5/5 for sigma=15/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001503
+Epoch [2/5], loss: 0.001208
+Epoch [3/5], loss: 0.001171
+Epoch [4/5], loss: 0.001144
+Epoch [5/5], loss: 0.001147
+
+[+] Training complete for sigma=15/255, gamma=2.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=15/255: 28.41 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma15_gamma2_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..3718c11ead10925dc71a6ce2626f756b66539356
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma0p5_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=25/255, gamma=0.5, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.008069
+Epoch [2/5], loss: 0.005961
+Epoch [3/5], loss: 0.003242
+Epoch [4/5], loss: 0.001745
+Epoch [5/5], loss: 0.001509
+
+--- Training stage 2/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.008200
+Epoch [2/5], loss: 0.007803
+Epoch [3/5], loss: 0.007229
+Epoch [4/5], loss: 0.006494
+Epoch [5/5], loss: 0.005941
+
+--- Training stage 3/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.017901
+Epoch [2/5], loss: 0.015068
+Epoch [3/5], loss: 0.010192
+Epoch [4/5], loss: 0.004888
+Epoch [5/5], loss: 0.002585
+
+--- Training stage 4/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.006524
+Epoch [2/5], loss: 0.006379
+Epoch [3/5], loss: 0.006303
+Epoch [4/5], loss: 0.006242
+Epoch [5/5], loss: 0.006026
+
+--- Training stage 5/5 for sigma=25/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.015867
+Epoch [2/5], loss: 0.013704
+Epoch [3/5], loss: 0.009813
+Epoch [4/5], loss: 0.005668
+Epoch [5/5], loss: 0.003713
+
+[+] Training complete for sigma=25/255, gamma=0.5, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 24.05 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma25_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..4b5e9f5ae348622130b0c9c92dde6eab53355349
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma0p5_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=25/255, gamma=0.5, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006620
+Epoch [2/5], loss: 0.002665
+Epoch [3/5], loss: 0.001568
+Epoch [4/5], loss: 0.001486
+Epoch [5/5], loss: 0.001450
+
+--- Training stage 2/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.007755
+Epoch [2/5], loss: 0.007122
+Epoch [3/5], loss: 0.006668
+Epoch [4/5], loss: 0.006446
+Epoch [5/5], loss: 0.006342
+
+--- Training stage 3/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.015253
+Epoch [2/5], loss: 0.007984
+Epoch [3/5], loss: 0.003271
+Epoch [4/5], loss: 0.002359
+Epoch [5/5], loss: 0.002215
+
+--- Training stage 4/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.007276
+Epoch [2/5], loss: 0.006981
+Epoch [3/5], loss: 0.006634
+Epoch [4/5], loss: 0.006373
+Epoch [5/5], loss: 0.006152
+
+--- Training stage 5/5 for sigma=25/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.014328
+Epoch [2/5], loss: 0.008166
+Epoch [3/5], loss: 0.004073
+Epoch [4/5], loss: 0.003354
+Epoch [5/5], loss: 0.003231
+
+[+] Training complete for sigma=25/255, gamma=0.5, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 24.43 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma25_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..b0355180de024660e069506a02acb606a177a0f4
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma0p5_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=25/255, gamma=0.5, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.003996
+Epoch [2/5], loss: 0.001629
+Epoch [3/5], loss: 0.001488
+Epoch [4/5], loss: 0.001463
+Epoch [5/5], loss: 0.001440
+
+--- Training stage 2/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.007076
+Epoch [2/5], loss: 0.006468
+Epoch [3/5], loss: 0.006264
+Epoch [4/5], loss: 0.006123
+Epoch [5/5], loss: 0.005988
+
+--- Training stage 3/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.008965
+Epoch [2/5], loss: 0.002829
+Epoch [3/5], loss: 0.002390
+Epoch [4/5], loss: 0.002256
+Epoch [5/5], loss: 0.002214
+
+--- Training stage 4/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.006379
+Epoch [2/5], loss: 0.005838
+Epoch [3/5], loss: 0.005541
+Epoch [4/5], loss: 0.005388
+Epoch [5/5], loss: 0.005266
+
+--- Training stage 5/5 for sigma=25/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.007657
+Epoch [2/5], loss: 0.003367
+Epoch [3/5], loss: 0.003147
+Epoch [4/5], loss: 0.003119
+Epoch [5/5], loss: 0.003068
+
+[+] Training complete for sigma=25/255, gamma=0.5, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 24.61 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma25_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..00af4ec90adcf82e8f17da75248f87b24fad50eb
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=25/255, gamma=1.0, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.008230
+Epoch [2/5], loss: 0.006214
+Epoch [3/5], loss: 0.003482
+Epoch [4/5], loss: 0.001832
+Epoch [5/5], loss: 0.001534
+
+--- Training stage 2/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.007782
+Epoch [2/5], loss: 0.007367
+Epoch [3/5], loss: 0.006801
+Epoch [4/5], loss: 0.006144
+Epoch [5/5], loss: 0.005608
+
+--- Training stage 3/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.016033
+Epoch [2/5], loss: 0.013565
+Epoch [3/5], loss: 0.009500
+Epoch [4/5], loss: 0.005011
+Epoch [5/5], loss: 0.002737
+
+--- Training stage 4/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.005688
+Epoch [2/5], loss: 0.005502
+Epoch [3/5], loss: 0.005430
+Epoch [4/5], loss: 0.005185
+Epoch [5/5], loss: 0.005062
+
+--- Training stage 5/5 for sigma=25/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.011693
+Epoch [2/5], loss: 0.010288
+Epoch [3/5], loss: 0.007889
+Epoch [4/5], loss: 0.005147
+Epoch [5/5], loss: 0.003678
+
+[+] Training complete for sigma=25/255, gamma=1.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 24.17 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma25_gamma1_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..8268ec72ad6b0b35e8ad4e0a063904794ad51685
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma1p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=25/255, gamma=1.0, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006473
+Epoch [2/5], loss: 0.002672
+Epoch [3/5], loss: 0.001565
+Epoch [4/5], loss: 0.001503
+Epoch [5/5], loss: 0.001460
+
+--- Training stage 2/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.007135
+Epoch [2/5], loss: 0.006459
+Epoch [3/5], loss: 0.005940
+Epoch [4/5], loss: 0.005666
+Epoch [5/5], loss: 0.005528
+
+--- Training stage 3/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.013210
+Epoch [2/5], loss: 0.007219
+Epoch [3/5], loss: 0.003049
+Epoch [4/5], loss: 0.002163
+Epoch [5/5], loss: 0.002094
+
+--- Training stage 4/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.005874
+Epoch [2/5], loss: 0.005602
+Epoch [3/5], loss: 0.005291
+Epoch [4/5], loss: 0.005048
+Epoch [5/5], loss: 0.004827
+
+--- Training stage 5/5 for sigma=25/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.010567
+Epoch [2/5], loss: 0.006413
+Epoch [3/5], loss: 0.003593
+Epoch [4/5], loss: 0.002981
+Epoch [5/5], loss: 0.002893
+
+[+] Training complete for sigma=25/255, gamma=1.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 24.90 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma25_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma1p0_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma1p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..88b612697f85bc0335a51a4f245d8c2428c16ddf
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma1p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=25/255, gamma=1.0, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.003953
+Epoch [2/5], loss: 0.001732
+Epoch [3/5], loss: 0.001525
+Epoch [4/5], loss: 0.001481
+Epoch [5/5], loss: 0.001446
+
+--- Training stage 2/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.006117
+Epoch [2/5], loss: 0.005532
+Epoch [3/5], loss: 0.005307
+Epoch [4/5], loss: 0.005111
+Epoch [5/5], loss: 0.004957
+
+--- Training stage 3/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.007860
+Epoch [2/5], loss: 0.002684
+Epoch [3/5], loss: 0.002239
+Epoch [4/5], loss: 0.002108
+Epoch [5/5], loss: 0.002055
+
+--- Training stage 4/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.004815
+Epoch [2/5], loss: 0.004411
+Epoch [3/5], loss: 0.004179
+Epoch [4/5], loss: 0.004021
+Epoch [5/5], loss: 0.003901
+
+--- Training stage 5/5 for sigma=25/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.005464
+Epoch [2/5], loss: 0.003012
+Epoch [3/5], loss: 0.002770
+Epoch [4/5], loss: 0.002723
+Epoch [5/5], loss: 0.002659
+
+[+] Training complete for sigma=25/255, gamma=1.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 25.15 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma25_gamma1_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma2p0_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma2p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..37349c1b767fbb468dbcebaa54e976caa6d934a9
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma2p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=25/255, gamma=2.0, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.008139
+Epoch [2/5], loss: 0.006194
+Epoch [3/5], loss: 0.003560
+Epoch [4/5], loss: 0.001858
+Epoch [5/5], loss: 0.001530
+
+--- Training stage 2/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.007157
+Epoch [2/5], loss: 0.006761
+Epoch [3/5], loss: 0.006189
+Epoch [4/5], loss: 0.005537
+Epoch [5/5], loss: 0.004999
+
+--- Training stage 3/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.013204
+Epoch [2/5], loss: 0.011239
+Epoch [3/5], loss: 0.008176
+Epoch [4/5], loss: 0.004759
+Epoch [5/5], loss: 0.002755
+
+--- Training stage 4/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.004541
+Epoch [2/5], loss: 0.004423
+Epoch [3/5], loss: 0.004311
+Epoch [4/5], loss: 0.004154
+Epoch [5/5], loss: 0.004066
+
+--- Training stage 5/5 for sigma=25/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.007951
+Epoch [2/5], loss: 0.007110
+Epoch [3/5], loss: 0.005892
+Epoch [4/5], loss: 0.004317
+Epoch [5/5], loss: 0.003322
+
+[+] Training complete for sigma=25/255, gamma=2.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 24.56 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma25_gamma2_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma2p0_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma2p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..fac86c9870ae95e2c530de1283c89b97a6a61cb1
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma2p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=25/255, gamma=2.0, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006863
+Epoch [2/5], loss: 0.003074
+Epoch [3/5], loss: 0.001646
+Epoch [4/5], loss: 0.001499
+Epoch [5/5], loss: 0.001486
+
+--- Training stage 2/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006196
+Epoch [2/5], loss: 0.005575
+Epoch [3/5], loss: 0.005095
+Epoch [4/5], loss: 0.004848
+Epoch [5/5], loss: 0.004683
+
+--- Training stage 3/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.009923
+Epoch [2/5], loss: 0.005790
+Epoch [3/5], loss: 0.002864
+Epoch [4/5], loss: 0.002099
+Epoch [5/5], loss: 0.001990
+
+--- Training stage 4/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.004552
+Epoch [2/5], loss: 0.004322
+Epoch [3/5], loss: 0.004059
+Epoch [4/5], loss: 0.003853
+Epoch [5/5], loss: 0.003649
+
+--- Training stage 5/5 for sigma=25/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006391
+Epoch [2/5], loss: 0.004356
+Epoch [3/5], loss: 0.002890
+Epoch [4/5], loss: 0.002541
+Epoch [5/5], loss: 0.002527
+
+[+] Training complete for sigma=25/255, gamma=2.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 25.47 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma25_gamma2_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma2p0_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma2p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..834d585f2e3165194b8fb0d431ff90eefa0c63bc
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma25_gamma2p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=25/255, gamma=2.0, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.004589
+Epoch [2/5], loss: 0.001688
+Epoch [3/5], loss: 0.001553
+Epoch [4/5], loss: 0.001493
+Epoch [5/5], loss: 0.001472
+
+--- Training stage 2/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.004852
+Epoch [2/5], loss: 0.004323
+Epoch [3/5], loss: 0.004079
+Epoch [4/5], loss: 0.003892
+Epoch [5/5], loss: 0.003716
+
+--- Training stage 3/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.004648
+Epoch [2/5], loss: 0.002175
+Epoch [3/5], loss: 0.001953
+Epoch [4/5], loss: 0.001896
+Epoch [5/5], loss: 0.001898
+
+--- Training stage 4/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.003228
+Epoch [2/5], loss: 0.002975
+Epoch [3/5], loss: 0.002771
+Epoch [4/5], loss: 0.002679
+Epoch [5/5], loss: 0.002593
+
+--- Training stage 5/5 for sigma=25/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.003104
+Epoch [2/5], loss: 0.002315
+Epoch [3/5], loss: 0.002208
+Epoch [4/5], loss: 0.002206
+Epoch [5/5], loss: 0.002176
+
+[+] Training complete for sigma=25/255, gamma=2.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=25/255: 25.95 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma25_gamma2_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..7c8c8cab303546d2ff1ff51820dc8f06fa9d3709
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma0p5_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=50/255, gamma=0.5, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.029584
+Epoch [2/5], loss: 0.023899
+Epoch [3/5], loss: 0.014700
+Epoch [4/5], loss: 0.006512
+Epoch [5/5], loss: 0.004170
+
+--- Training stage 2/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.028713
+Epoch [2/5], loss: 0.028175
+Epoch [3/5], loss: 0.027336
+Epoch [4/5], loss: 0.026502
+Epoch [5/5], loss: 0.025657
+
+--- Training stage 3/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.084104
+Epoch [2/5], loss: 0.073772
+Epoch [3/5], loss: 0.054880
+Epoch [4/5], loss: 0.028826
+Epoch [5/5], loss: 0.010765
+
+--- Training stage 4/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.021449
+Epoch [2/5], loss: 0.021367
+Epoch [3/5], loss: 0.020970
+Epoch [4/5], loss: 0.020541
+Epoch [5/5], loss: 0.020303
+
+--- Training stage 5/5 for sigma=50/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.060313
+Epoch [2/5], loss: 0.052708
+Epoch [3/5], loss: 0.039311
+Epoch [4/5], loss: 0.022190
+Epoch [5/5], loss: 0.011634
+
+[+] Training complete for sigma=50/255, gamma=0.5, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 20.00 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma50_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..0c429682edede405a302d19d11e78f6bf396c814
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma0p5_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=50/255, gamma=0.5, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.024913
+Epoch [2/5], loss: 0.011433
+Epoch [3/5], loss: 0.004776
+Epoch [4/5], loss: 0.004149
+Epoch [5/5], loss: 0.004021
+
+--- Training stage 2/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.028070
+Epoch [2/5], loss: 0.027052
+Epoch [3/5], loss: 0.026144
+Epoch [4/5], loss: 0.025454
+Epoch [5/5], loss: 0.024758
+
+--- Training stage 3/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.072854
+Epoch [2/5], loss: 0.043862
+Epoch [3/5], loss: 0.015624
+Epoch [4/5], loss: 0.006678
+Epoch [5/5], loss: 0.006081
+
+--- Training stage 4/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.027335
+Epoch [2/5], loss: 0.024790
+Epoch [3/5], loss: 0.022788
+Epoch [4/5], loss: 0.021913
+Epoch [5/5], loss: 0.021480
+
+--- Training stage 5/5 for sigma=50/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.062573
+Epoch [2/5], loss: 0.038249
+Epoch [3/5], loss: 0.015860
+Epoch [4/5], loss: 0.009665
+Epoch [5/5], loss: 0.009207
+
+[+] Training complete for sigma=50/255, gamma=0.5, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 20.25 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma50_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..9b6c1feeb7ca74b33d32211e9ea9b1eda0ba96ab
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma0p5_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=50/255, gamma=0.5, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.015762
+Epoch [2/5], loss: 0.004558
+Epoch [3/5], loss: 0.003976
+Epoch [4/5], loss: 0.003867
+Epoch [5/5], loss: 0.003815
+
+--- Training stage 2/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.025326
+Epoch [2/5], loss: 0.023677
+Epoch [3/5], loss: 0.022823
+Epoch [4/5], loss: 0.022212
+Epoch [5/5], loss: 0.021572
+
+--- Training stage 3/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.034792
+Epoch [2/5], loss: 0.007874
+Epoch [3/5], loss: 0.005510
+Epoch [4/5], loss: 0.005342
+Epoch [5/5], loss: 0.005194
+
+--- Training stage 4/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.020218
+Epoch [2/5], loss: 0.017995
+Epoch [3/5], loss: 0.017404
+Epoch [4/5], loss: 0.017063
+Epoch [5/5], loss: 0.016663
+
+--- Training stage 5/5 for sigma=50/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.031694
+Epoch [2/5], loss: 0.009578
+Epoch [3/5], loss: 0.007470
+Epoch [4/5], loss: 0.007141
+Epoch [5/5], loss: 0.006905
+
+[+] Training complete for sigma=50/255, gamma=0.5, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 21.42 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma50_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..7c42eb9e4fb6470f566b60e3cec7544756a78821
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=50/255, gamma=1.0, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.029464
+Epoch [2/5], loss: 0.023969
+Epoch [3/5], loss: 0.014966
+Epoch [4/5], loss: 0.006769
+Epoch [5/5], loss: 0.004240
+
+--- Training stage 2/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.026967
+Epoch [2/5], loss: 0.026353
+Epoch [3/5], loss: 0.025508
+Epoch [4/5], loss: 0.024426
+Epoch [5/5], loss: 0.023418
+
+--- Training stage 3/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.073761
+Epoch [2/5], loss: 0.064624
+Epoch [3/5], loss: 0.048640
+Epoch [4/5], loss: 0.026830
+Epoch [5/5], loss: 0.010707
+
+--- Training stage 4/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.016914
+Epoch [2/5], loss: 0.016620
+Epoch [3/5], loss: 0.016312
+Epoch [4/5], loss: 0.015891
+Epoch [5/5], loss: 0.015462
+
+--- Training stage 5/5 for sigma=50/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.042128
+Epoch [2/5], loss: 0.036830
+Epoch [3/5], loss: 0.028203
+Epoch [4/5], loss: 0.017064
+Epoch [5/5], loss: 0.010237
+
+[+] Training complete for sigma=50/255, gamma=1.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 20.50 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma50_gamma1_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..4fbef0f4923fa56372441550615960516b550af3
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma1p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=50/255, gamma=1.0, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.024881
+Epoch [2/5], loss: 0.011873
+Epoch [3/5], loss: 0.004839
+Epoch [4/5], loss: 0.004015
+Epoch [5/5], loss: 0.003961
+
+--- Training stage 2/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.025556
+Epoch [2/5], loss: 0.024231
+Epoch [3/5], loss: 0.023122
+Epoch [4/5], loss: 0.022191
+Epoch [5/5], loss: 0.021474
+
+--- Training stage 3/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.057844
+Epoch [2/5], loss: 0.034933
+Epoch [3/5], loss: 0.013406
+Epoch [4/5], loss: 0.006116
+Epoch [5/5], loss: 0.005589
+
+--- Training stage 4/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.022003
+Epoch [2/5], loss: 0.020010
+Epoch [3/5], loss: 0.018355
+Epoch [4/5], loss: 0.017399
+Epoch [5/5], loss: 0.016999
+
+--- Training stage 5/5 for sigma=50/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.042500
+Epoch [2/5], loss: 0.025925
+Epoch [3/5], loss: 0.011964
+Epoch [4/5], loss: 0.007993
+Epoch [5/5], loss: 0.007723
+
+[+] Training complete for sigma=50/255, gamma=1.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 20.94 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma50_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma1p0_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma1p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..85fbeaf84f5f44ced1d72432826623288bf4cf3e
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma1p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=50/255, gamma=1.0, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.017001
+Epoch [2/5], loss: 0.004774
+Epoch [3/5], loss: 0.004109
+Epoch [4/5], loss: 0.003889
+Epoch [5/5], loss: 0.003761
+
+--- Training stage 2/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.021717
+Epoch [2/5], loss: 0.020158
+Epoch [3/5], loss: 0.019234
+Epoch [4/5], loss: 0.018355
+Epoch [5/5], loss: 0.017549
+
+--- Training stage 3/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.025366
+Epoch [2/5], loss: 0.006771
+Epoch [3/5], loss: 0.005024
+Epoch [4/5], loss: 0.004846
+Epoch [5/5], loss: 0.004834
+
+--- Training stage 4/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.014374
+Epoch [2/5], loss: 0.012712
+Epoch [3/5], loss: 0.012013
+Epoch [4/5], loss: 0.011703
+Epoch [5/5], loss: 0.011316
+
+--- Training stage 5/5 for sigma=50/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.021388
+Epoch [2/5], loss: 0.007736
+Epoch [3/5], loss: 0.006047
+Epoch [4/5], loss: 0.005830
+Epoch [5/5], loss: 0.005793
+
+[+] Training complete for sigma=50/255, gamma=1.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 22.12 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma50_gamma1_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma2p0_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma2p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..c35362358ce644e9f3bdb491c3bfad37c4fde16d
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma2p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=50/255, gamma=2.0, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.030023
+Epoch [2/5], loss: 0.025010
+Epoch [3/5], loss: 0.016653
+Epoch [4/5], loss: 0.007893
+Epoch [5/5], loss: 0.004397
+
+--- Training stage 2/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.023650
+Epoch [2/5], loss: 0.022720
+Epoch [3/5], loss: 0.021673
+Epoch [4/5], loss: 0.020400
+Epoch [5/5], loss: 0.019317
+
+--- Training stage 3/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.056338
+Epoch [2/5], loss: 0.049453
+Epoch [3/5], loss: 0.037735
+Epoch [4/5], loss: 0.022141
+Epoch [5/5], loss: 0.009801
+
+--- Training stage 4/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.011694
+Epoch [2/5], loss: 0.011542
+Epoch [3/5], loss: 0.011198
+Epoch [4/5], loss: 0.010810
+Epoch [5/5], loss: 0.010670
+
+--- Training stage 5/5 for sigma=50/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.024129
+Epoch [2/5], loss: 0.021478
+Epoch [3/5], loss: 0.017339
+Epoch [4/5], loss: 0.011915
+Epoch [5/5], loss: 0.008267
+
+[+] Training complete for sigma=50/255, gamma=2.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 21.27 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma50_gamma2_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma2p0_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma2p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..ab0e046140096ffe35cc6e787cf7081a9d12ff08
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma2p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=50/255, gamma=2.0, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.027320
+Epoch [2/5], loss: 0.014999
+Epoch [3/5], loss: 0.005928
+Epoch [4/5], loss: 0.004115
+Epoch [5/5], loss: 0.003963
+
+--- Training stage 2/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.022152
+Epoch [2/5], loss: 0.020738
+Epoch [3/5], loss: 0.019506
+Epoch [4/5], loss: 0.018442
+Epoch [5/5], loss: 0.017498
+
+--- Training stage 3/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.042837
+Epoch [2/5], loss: 0.026445
+Epoch [3/5], loss: 0.011239
+Epoch [4/5], loss: 0.005576
+Epoch [5/5], loss: 0.005045
+
+--- Training stage 4/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.015354
+Epoch [2/5], loss: 0.014034
+Epoch [3/5], loss: 0.012864
+Epoch [4/5], loss: 0.011943
+Epoch [5/5], loss: 0.011437
+
+--- Training stage 5/5 for sigma=50/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.025148
+Epoch [2/5], loss: 0.016082
+Epoch [3/5], loss: 0.008509
+Epoch [4/5], loss: 0.006176
+Epoch [5/5], loss: 0.005883
+
+[+] Training complete for sigma=50/255, gamma=2.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 22.09 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma50_gamma2_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma2p0_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma2p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..675fdb390a8b6c330c35b0737b927fee5e295c06
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma50_gamma2p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=50/255, gamma=2.0, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.019294
+Epoch [2/5], loss: 0.005441
+Epoch [3/5], loss: 0.004222
+Epoch [4/5], loss: 0.003961
+Epoch [5/5], loss: 0.003846
+
+--- Training stage 2/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.016679
+Epoch [2/5], loss: 0.015335
+Epoch [3/5], loss: 0.014296
+Epoch [4/5], loss: 0.013433
+Epoch [5/5], loss: 0.012683
+
+--- Training stage 3/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.019161
+Epoch [2/5], loss: 0.006153
+Epoch [3/5], loss: 0.004517
+Epoch [4/5], loss: 0.004490
+Epoch [5/5], loss: 0.004434
+
+--- Training stage 4/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.008783
+Epoch [2/5], loss: 0.007799
+Epoch [3/5], loss: 0.007329
+Epoch [4/5], loss: 0.007125
+Epoch [5/5], loss: 0.006798
+
+--- Training stage 5/5 for sigma=50/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.009858
+Epoch [2/5], loss: 0.005365
+Epoch [3/5], loss: 0.004707
+Epoch [4/5], loss: 0.004757
+Epoch [5/5], loss: 0.004655
+
+[+] Training complete for sigma=50/255, gamma=2.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=50/255: 23.02 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma50_gamma2_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma0p5_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma0p5_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..32a5aa5f82c5e5fcbb5b83d9b6a9f9f08dc41bbf
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma0p5_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=75/255, gamma=0.5, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.057852
+Epoch [2/5], loss: 0.049021
+Epoch [3/5], loss: 0.033257
+Epoch [4/5], loss: 0.015868
+Epoch [5/5], loss: 0.008473
+
+--- Training stage 2/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.052815
+Epoch [2/5], loss: 0.052044
+Epoch [3/5], loss: 0.051170
+Epoch [4/5], loss: 0.049853
+Epoch [5/5], loss: 0.048880
+
+--- Training stage 3/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.163022
+Epoch [2/5], loss: 0.147083
+Epoch [3/5], loss: 0.116171
+Epoch [4/5], loss: 0.069430
+Epoch [5/5], loss: 0.026966
+
+--- Training stage 4/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.029874
+Epoch [2/5], loss: 0.029423
+Epoch [3/5], loss: 0.029001
+Epoch [4/5], loss: 0.028396
+Epoch [5/5], loss: 0.027912
+
+--- Training stage 5/5 for sigma=75/255, gamma=0.5, tau_init=0.05 ---
+Epoch [1/5], loss: 0.082389
+Epoch [2/5], loss: 0.073945
+Epoch [3/5], loss: 0.057524
+Epoch [4/5], loss: 0.036234
+Epoch [5/5], loss: 0.021477
+
+[+] Training complete for sigma=75/255, gamma=0.5, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 17.46 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma75_gamma0p5_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma0p5_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma0p5_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..4f6aa5fe9085fbe29558c3e3cb2016b1223f3d26
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma0p5_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=75/255, gamma=0.5, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.050189
+Epoch [2/5], loss: 0.026985
+Epoch [3/5], loss: 0.010839
+Epoch [4/5], loss: 0.008004
+Epoch [5/5], loss: 0.007704
+
+--- Training stage 2/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.054382
+Epoch [2/5], loss: 0.052797
+Epoch [3/5], loss: 0.051172
+Epoch [4/5], loss: 0.049414
+Epoch [5/5], loss: 0.047790
+
+--- Training stage 3/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.143748
+Epoch [2/5], loss: 0.095963
+Epoch [3/5], loss: 0.040607
+Epoch [4/5], loss: 0.014379
+Epoch [5/5], loss: 0.011454
+
+--- Training stage 4/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.053129
+Epoch [2/5], loss: 0.047063
+Epoch [3/5], loss: 0.041984
+Epoch [4/5], loss: 0.040170
+Epoch [5/5], loss: 0.039479
+
+--- Training stage 5/5 for sigma=75/255, gamma=0.5, tau_init=0.1 ---
+Epoch [1/5], loss: 0.120434
+Epoch [2/5], loss: 0.079839
+Epoch [3/5], loss: 0.036286
+Epoch [4/5], loss: 0.018542
+Epoch [5/5], loss: 0.016548
+
+[+] Training complete for sigma=75/255, gamma=0.5, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 17.80 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma75_gamma0p5_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma0p5_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma0p5_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..40c1b97748f5279d4ea23f052a7fdf4ae00f14c4
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma0p5_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 0.5
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=75/255, gamma=0.5, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.035506
+Epoch [2/5], loss: 0.009437
+Epoch [3/5], loss: 0.007582
+Epoch [4/5], loss: 0.007189
+Epoch [5/5], loss: 0.006979
+
+--- Training stage 2/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.049698
+Epoch [2/5], loss: 0.047136
+Epoch [3/5], loss: 0.045300
+Epoch [4/5], loss: 0.043781
+Epoch [5/5], loss: 0.042411
+
+--- Training stage 3/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.088353
+Epoch [2/5], loss: 0.022623
+Epoch [3/5], loss: 0.010782
+Epoch [4/5], loss: 0.009966
+Epoch [5/5], loss: 0.009371
+
+--- Training stage 4/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.038915
+Epoch [2/5], loss: 0.033784
+Epoch [3/5], loss: 0.032387
+Epoch [4/5], loss: 0.031540
+Epoch [5/5], loss: 0.030715
+
+--- Training stage 5/5 for sigma=75/255, gamma=0.5, tau_init=0.2 ---
+Epoch [1/5], loss: 0.065630
+Epoch [2/5], loss: 0.019794
+Epoch [3/5], loss: 0.013275
+Epoch [4/5], loss: 0.012494
+Epoch [5/5], loss: 0.011664
+
+[+] Training complete for sigma=75/255, gamma=0.5, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 19.30 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma75_gamma0p5_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma1p0_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma1p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..3462ee122ad259583ca48f909a3a3f2b3be8b484
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma1p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=75/255, gamma=1.0, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.058165
+Epoch [2/5], loss: 0.049459
+Epoch [3/5], loss: 0.034089
+Epoch [4/5], loss: 0.016818
+Epoch [5/5], loss: 0.008822
+
+--- Training stage 2/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.048791
+Epoch [2/5], loss: 0.047784
+Epoch [3/5], loss: 0.046690
+Epoch [4/5], loss: 0.045440
+Epoch [5/5], loss: 0.044064
+
+--- Training stage 3/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.141191
+Epoch [2/5], loss: 0.127216
+Epoch [3/5], loss: 0.100910
+Epoch [4/5], loss: 0.061601
+Epoch [5/5], loss: 0.025592
+
+--- Training stage 4/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.024070
+Epoch [2/5], loss: 0.023545
+Epoch [3/5], loss: 0.022989
+Epoch [4/5], loss: 0.022211
+Epoch [5/5], loss: 0.021758
+
+--- Training stage 5/5 for sigma=75/255, gamma=1.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.057846
+Epoch [2/5], loss: 0.052135
+Epoch [3/5], loss: 0.041903
+Epoch [4/5], loss: 0.028457
+Epoch [5/5], loss: 0.018823
+
+[+] Training complete for sigma=75/255, gamma=1.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 17.96 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma75_gamma1_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma1p0_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma1p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..957e43955b2e50bbb1ed97ddbabbfd1863403950
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma1p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=75/255, gamma=1.0, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.053571
+Epoch [2/5], loss: 0.031286
+Epoch [3/5], loss: 0.012493
+Epoch [4/5], loss: 0.008071
+Epoch [5/5], loss: 0.007586
+
+--- Training stage 2/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.050128
+Epoch [2/5], loss: 0.048271
+Epoch [3/5], loss: 0.046292
+Epoch [4/5], loss: 0.044221
+Epoch [5/5], loss: 0.042220
+
+--- Training stage 3/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.118480
+Epoch [2/5], loss: 0.078920
+Epoch [3/5], loss: 0.034755
+Epoch [4/5], loss: 0.013302
+Epoch [5/5], loss: 0.010358
+
+--- Training stage 4/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.042871
+Epoch [2/5], loss: 0.037839
+Epoch [3/5], loss: 0.033429
+Epoch [4/5], loss: 0.031402
+Epoch [5/5], loss: 0.030457
+
+--- Training stage 5/5 for sigma=75/255, gamma=1.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.084284
+Epoch [2/5], loss: 0.055247
+Epoch [3/5], loss: 0.026082
+Epoch [4/5], loss: 0.014758
+Epoch [5/5], loss: 0.013410
+
+[+] Training complete for sigma=75/255, gamma=1.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 18.67 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma75_gamma1_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma1p0_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma1p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..8f181cf5aff154f5865a9082758809454de88bb7
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma1p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 1.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=75/255, gamma=1.0, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.034710
+Epoch [2/5], loss: 0.009676
+Epoch [3/5], loss: 0.007555
+Epoch [4/5], loss: 0.007300
+Epoch [5/5], loss: 0.007019
+
+--- Training stage 2/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.041823
+Epoch [2/5], loss: 0.039380
+Epoch [3/5], loss: 0.037450
+Epoch [4/5], loss: 0.035607
+Epoch [5/5], loss: 0.034119
+
+--- Training stage 3/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.067157
+Epoch [2/5], loss: 0.017761
+Epoch [3/5], loss: 0.009255
+Epoch [4/5], loss: 0.008659
+Epoch [5/5], loss: 0.008480
+
+--- Training stage 4/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.027590
+Epoch [2/5], loss: 0.023646
+Epoch [3/5], loss: 0.022559
+Epoch [4/5], loss: 0.021713
+Epoch [5/5], loss: 0.020840
+
+--- Training stage 5/5 for sigma=75/255, gamma=1.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.036351
+Epoch [2/5], loss: 0.012762
+Epoch [3/5], loss: 0.010132
+Epoch [4/5], loss: 0.009803
+Epoch [5/5], loss: 0.009565
+
+[+] Training complete for sigma=75/255, gamma=1.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 20.20 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma75_gamma1_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma2p0_tau0p05.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma2p0_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..3d4e685df6a5207fa5c2959f11221f14ae0ec31e
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma2p0_tau0p05.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=75/255, gamma=2.0, tau_init=0.05...
+
+--- Training stage 1/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.058594
+Epoch [2/5], loss: 0.050859
+Epoch [3/5], loss: 0.036624
+Epoch [4/5], loss: 0.019286
+Epoch [5/5], loss: 0.009415
+
+--- Training stage 2/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.041026
+Epoch [2/5], loss: 0.039564
+Epoch [3/5], loss: 0.037881
+Epoch [4/5], loss: 0.036149
+Epoch [5/5], loss: 0.034752
+
+--- Training stage 3/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.102114
+Epoch [2/5], loss: 0.091430
+Epoch [3/5], loss: 0.072924
+Epoch [4/5], loss: 0.046754
+Epoch [5/5], loss: 0.022245
+
+--- Training stage 4/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.017598
+Epoch [2/5], loss: 0.017158
+Epoch [3/5], loss: 0.016404
+Epoch [4/5], loss: 0.015986
+Epoch [5/5], loss: 0.015527
+
+--- Training stage 5/5 for sigma=75/255, gamma=2.0, tau_init=0.05 ---
+Epoch [1/5], loss: 0.036148
+Epoch [2/5], loss: 0.033044
+Epoch [3/5], loss: 0.027915
+Epoch [4/5], loss: 0.020955
+Epoch [5/5], loss: 0.015757
+
+[+] Training complete for sigma=75/255, gamma=2.0, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 18.74 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma75_gamma2_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma2p0_tau0p1.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma2p0_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..06626f5d6b9459d8d46b9dd2d95ff83bb7ac5d6c
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma2p0_tau0p1.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=75/255, gamma=2.0, tau_init=0.1...
+
+--- Training stage 1/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.052994
+Epoch [2/5], loss: 0.031976
+Epoch [3/5], loss: 0.013421
+Epoch [4/5], loss: 0.008061
+Epoch [5/5], loss: 0.007522
+
+--- Training stage 2/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.042653
+Epoch [2/5], loss: 0.040402
+Epoch [3/5], loss: 0.038180
+Epoch [4/5], loss: 0.035988
+Epoch [5/5], loss: 0.033915
+
+--- Training stage 3/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.083601
+Epoch [2/5], loss: 0.055882
+Epoch [3/5], loss: 0.026480
+Epoch [4/5], loss: 0.011466
+Epoch [5/5], loss: 0.009249
+
+--- Training stage 4/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.029375
+Epoch [2/5], loss: 0.026224
+Epoch [3/5], loss: 0.023377
+Epoch [4/5], loss: 0.021759
+Epoch [5/5], loss: 0.020863
+
+--- Training stage 5/5 for sigma=75/255, gamma=2.0, tau_init=0.1 ---
+Epoch [1/5], loss: 0.048918
+Epoch [2/5], loss: 0.032294
+Epoch [3/5], loss: 0.016871
+Epoch [4/5], loss: 0.011068
+Epoch [5/5], loss: 0.010378
+
+[+] Training complete for sigma=75/255, gamma=2.0, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 19.82 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma75_gamma2_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma2p0_tau0p2.log b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma2p0_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..84e7759bfb1f405026c66b3ab1ae858b326b96dc
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_rbf_wave_sigma75_gamma2p0_tau0p2.log
@@ -0,0 +1,46 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Gamma sweep: 2.0
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage Telegraph model with RBF activation for sigma=75/255, gamma=2.0, tau_init=0.2...
+
+--- Training stage 1/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.038197
+Epoch [2/5], loss: 0.011543
+Epoch [3/5], loss: 0.008041
+Epoch [4/5], loss: 0.007537
+Epoch [5/5], loss: 0.007131
+
+--- Training stage 2/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.032962
+Epoch [2/5], loss: 0.030302
+Epoch [3/5], loss: 0.028116
+Epoch [4/5], loss: 0.026279
+Epoch [5/5], loss: 0.024666
+
+--- Training stage 3/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.035899
+Epoch [2/5], loss: 0.011761
+Epoch [3/5], loss: 0.008134
+Epoch [4/5], loss: 0.007803
+Epoch [5/5], loss: 0.007733
+
+--- Training stage 4/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.016792
+Epoch [2/5], loss: 0.014304
+Epoch [3/5], loss: 0.013484
+Epoch [4/5], loss: 0.012841
+Epoch [5/5], loss: 0.012458
+
+--- Training stage 5/5 for sigma=75/255, gamma=2.0, tau_init=0.2 ---
+Epoch [1/5], loss: 0.016768
+Epoch [2/5], loss: 0.008650
+Epoch [3/5], loss: 0.007936
+Epoch [4/5], loss: 0.007771
+Epoch [5/5], loss: 0.007806
+
+[+] Training complete for sigma=75/255, gamma=2.0, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final RBF model PSNR on BSD68 at sigma=75/255: 21.08 dB
+[+] Saved checkpoint: rbf_model_5stages_waveTrue_sigma75_gamma2_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_tnrd_sigma15_tau0p05.log b/logs/overnight_20260408_193023/train_tnrd_sigma15_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..76401937bc1a0372715aba9155efe52cf01d0e96
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_tnrd_sigma15_tau0p05.log
@@ -0,0 +1,45 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage TNRD baseline for sigma=15/255, tau_init=0.05...
+
+--- Training TNRD stage 1/5 for sigma=15/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001272
+Epoch [2/5], loss: 0.000751
+Epoch [3/5], loss: 0.000725
+Epoch [4/5], loss: 0.000735
+Epoch [5/5], loss: 0.000709
+
+--- Training TNRD stage 2/5 for sigma=15/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.000716
+Epoch [2/5], loss: 0.000711
+Epoch [3/5], loss: 0.000709
+Epoch [4/5], loss: 0.000702
+Epoch [5/5], loss: 0.000701
+
+--- Training TNRD stage 3/5 for sigma=15/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.000702
+Epoch [2/5], loss: 0.000698
+Epoch [3/5], loss: 0.000705
+Epoch [4/5], loss: 0.000705
+Epoch [5/5], loss: 0.000706
+
+--- Training TNRD stage 4/5 for sigma=15/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.000705
+Epoch [2/5], loss: 0.000697
+Epoch [3/5], loss: 0.000705
+Epoch [4/5], loss: 0.000703
+Epoch [5/5], loss: 0.000708
+
+--- Training TNRD stage 5/5 for sigma=15/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.000702
+Epoch [2/5], loss: 0.000704
+Epoch [3/5], loss: 0.000705
+Epoch [4/5], loss: 0.000709
+Epoch [5/5], loss: 0.000699
+
+[+] Training complete for sigma=15/255, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final TNRD baseline PSNR on BSD68 at sigma=15/255: 30.58 dB
+[+] Saved checkpoint: tnrd_baseline_5stages_sigma15_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_tnrd_sigma15_tau0p1.log b/logs/overnight_20260408_193023/train_tnrd_sigma15_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..210ed911361999561980433a0c88245d86a37f1d
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_tnrd_sigma15_tau0p1.log
@@ -0,0 +1,45 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage TNRD baseline for sigma=15/255, tau_init=0.1...
+
+--- Training TNRD stage 1/5 for sigma=15/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001025
+Epoch [2/5], loss: 0.000736
+Epoch [3/5], loss: 0.000719
+Epoch [4/5], loss: 0.000715
+Epoch [5/5], loss: 0.000711
+
+--- Training TNRD stage 2/5 for sigma=15/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.000707
+Epoch [2/5], loss: 0.000703
+Epoch [3/5], loss: 0.000706
+Epoch [4/5], loss: 0.000711
+Epoch [5/5], loss: 0.000702
+
+--- Training TNRD stage 3/5 for sigma=15/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.000704
+Epoch [2/5], loss: 0.000702
+Epoch [3/5], loss: 0.000699
+Epoch [4/5], loss: 0.000713
+Epoch [5/5], loss: 0.000704
+
+--- Training TNRD stage 4/5 for sigma=15/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.000698
+Epoch [2/5], loss: 0.000703
+Epoch [3/5], loss: 0.000703
+Epoch [4/5], loss: 0.000698
+Epoch [5/5], loss: 0.000709
+
+--- Training TNRD stage 5/5 for sigma=15/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.000706
+Epoch [2/5], loss: 0.000707
+Epoch [3/5], loss: 0.000702
+Epoch [4/5], loss: 0.000699
+Epoch [5/5], loss: 0.000705
+
+[+] Training complete for sigma=15/255, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final TNRD baseline PSNR on BSD68 at sigma=15/255: 30.59 dB
+[+] Saved checkpoint: tnrd_baseline_5stages_sigma15_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_tnrd_sigma15_tau0p2.log b/logs/overnight_20260408_193023/train_tnrd_sigma15_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..e55f17a8b669ed9252079b30cbbeb02e5eb61ae0
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_tnrd_sigma15_tau0p2.log
@@ -0,0 +1,45 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 15
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage TNRD baseline for sigma=15/255, tau_init=0.2...
+
+--- Training TNRD stage 1/5 for sigma=15/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.000890
+Epoch [2/5], loss: 0.000718
+Epoch [3/5], loss: 0.000719
+Epoch [4/5], loss: 0.000713
+Epoch [5/5], loss: 0.000712
+
+--- Training TNRD stage 2/5 for sigma=15/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.000707
+Epoch [2/5], loss: 0.000709
+Epoch [3/5], loss: 0.000700
+Epoch [4/5], loss: 0.000704
+Epoch [5/5], loss: 0.000705
+
+--- Training TNRD stage 3/5 for sigma=15/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.000703
+Epoch [2/5], loss: 0.000704
+Epoch [3/5], loss: 0.000706
+Epoch [4/5], loss: 0.000703
+Epoch [5/5], loss: 0.000701
+
+--- Training TNRD stage 4/5 for sigma=15/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.000701
+Epoch [2/5], loss: 0.000702
+Epoch [3/5], loss: 0.000695
+Epoch [4/5], loss: 0.000706
+Epoch [5/5], loss: 0.000710
+
+--- Training TNRD stage 5/5 for sigma=15/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.000710
+Epoch [2/5], loss: 0.000709
+Epoch [3/5], loss: 0.000702
+Epoch [4/5], loss: 0.000714
+Epoch [5/5], loss: 0.000704
+
+[+] Training complete for sigma=15/255, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final TNRD baseline PSNR on BSD68 at sigma=15/255: 30.59 dB
+[+] Saved checkpoint: tnrd_baseline_5stages_sigma15_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_tnrd_sigma25_tau0p05.log b/logs/overnight_20260408_193023/train_tnrd_sigma25_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..fc7c032afddc0898732aa443c25676ac36a75f89
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_tnrd_sigma25_tau0p05.log
@@ -0,0 +1,45 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage TNRD baseline for sigma=25/255, tau_init=0.05...
+
+--- Training TNRD stage 1/5 for sigma=25/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003171
+Epoch [2/5], loss: 0.001466
+Epoch [3/5], loss: 0.001430
+Epoch [4/5], loss: 0.001432
+Epoch [5/5], loss: 0.001420
+
+--- Training TNRD stage 2/5 for sigma=25/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001399
+Epoch [2/5], loss: 0.001366
+Epoch [3/5], loss: 0.001364
+Epoch [4/5], loss: 0.001350
+Epoch [5/5], loss: 0.001338
+
+--- Training TNRD stage 3/5 for sigma=25/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001360
+Epoch [2/5], loss: 0.001322
+Epoch [3/5], loss: 0.001340
+Epoch [4/5], loss: 0.001345
+Epoch [5/5], loss: 0.001345
+
+--- Training TNRD stage 4/5 for sigma=25/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001344
+Epoch [2/5], loss: 0.001328
+Epoch [3/5], loss: 0.001325
+Epoch [4/5], loss: 0.001336
+Epoch [5/5], loss: 0.001342
+
+--- Training TNRD stage 5/5 for sigma=25/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.001335
+Epoch [2/5], loss: 0.001325
+Epoch [3/5], loss: 0.001341
+Epoch [4/5], loss: 0.001339
+Epoch [5/5], loss: 0.001349
+
+[+] Training complete for sigma=25/255, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final TNRD baseline PSNR on BSD68 at sigma=25/255: 27.94 dB
+[+] Saved checkpoint: tnrd_baseline_5stages_sigma25_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_tnrd_sigma25_tau0p1.log b/logs/overnight_20260408_193023/train_tnrd_sigma25_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..94ce1cf792e2d8a43db66658768e66847339a664
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_tnrd_sigma25_tau0p1.log
@@ -0,0 +1,45 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage TNRD baseline for sigma=25/255, tau_init=0.1...
+
+--- Training TNRD stage 1/5 for sigma=25/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.002323
+Epoch [2/5], loss: 0.001439
+Epoch [3/5], loss: 0.001409
+Epoch [4/5], loss: 0.001421
+Epoch [5/5], loss: 0.001400
+
+--- Training TNRD stage 2/5 for sigma=25/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001364
+Epoch [2/5], loss: 0.001364
+Epoch [3/5], loss: 0.001346
+Epoch [4/5], loss: 0.001340
+Epoch [5/5], loss: 0.001342
+
+--- Training TNRD stage 3/5 for sigma=25/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001330
+Epoch [2/5], loss: 0.001336
+Epoch [3/5], loss: 0.001327
+Epoch [4/5], loss: 0.001344
+Epoch [5/5], loss: 0.001333
+
+--- Training TNRD stage 4/5 for sigma=25/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001335
+Epoch [2/5], loss: 0.001331
+Epoch [3/5], loss: 0.001335
+Epoch [4/5], loss: 0.001325
+Epoch [5/5], loss: 0.001347
+
+--- Training TNRD stage 5/5 for sigma=25/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.001330
+Epoch [2/5], loss: 0.001337
+Epoch [3/5], loss: 0.001346
+Epoch [4/5], loss: 0.001347
+Epoch [5/5], loss: 0.001327
+
+[+] Training complete for sigma=25/255, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final TNRD baseline PSNR on BSD68 at sigma=25/255: 27.94 dB
+[+] Saved checkpoint: tnrd_baseline_5stages_sigma25_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_tnrd_sigma25_tau0p2.log b/logs/overnight_20260408_193023/train_tnrd_sigma25_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..7b3441f659fc982179549b3e10e0579eb7fdfe18
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_tnrd_sigma25_tau0p2.log
@@ -0,0 +1,45 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 25
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage TNRD baseline for sigma=25/255, tau_init=0.2...
+
+--- Training TNRD stage 1/5 for sigma=25/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001912
+Epoch [2/5], loss: 0.001415
+Epoch [3/5], loss: 0.001413
+Epoch [4/5], loss: 0.001424
+Epoch [5/5], loss: 0.001391
+
+--- Training TNRD stage 2/5 for sigma=25/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001361
+Epoch [2/5], loss: 0.001357
+Epoch [3/5], loss: 0.001343
+Epoch [4/5], loss: 0.001335
+Epoch [5/5], loss: 0.001327
+
+--- Training TNRD stage 3/5 for sigma=25/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001325
+Epoch [2/5], loss: 0.001346
+Epoch [3/5], loss: 0.001332
+Epoch [4/5], loss: 0.001341
+Epoch [5/5], loss: 0.001344
+
+--- Training TNRD stage 4/5 for sigma=25/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001330
+Epoch [2/5], loss: 0.001345
+Epoch [3/5], loss: 0.001321
+Epoch [4/5], loss: 0.001349
+Epoch [5/5], loss: 0.001317
+
+--- Training TNRD stage 5/5 for sigma=25/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.001338
+Epoch [2/5], loss: 0.001339
+Epoch [3/5], loss: 0.001333
+Epoch [4/5], loss: 0.001337
+Epoch [5/5], loss: 0.001340
+
+[+] Training complete for sigma=25/255, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final TNRD baseline PSNR on BSD68 at sigma=25/255: 27.94 dB
+[+] Saved checkpoint: tnrd_baseline_5stages_sigma25_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_tnrd_sigma50_tau0p05.log b/logs/overnight_20260408_193023/train_tnrd_sigma50_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..80db51d320fcfa5b346e7a9a09812b04426bad5f
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_tnrd_sigma50_tau0p05.log
@@ -0,0 +1,45 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage TNRD baseline for sigma=50/255, tau_init=0.05...
+
+--- Training TNRD stage 1/5 for sigma=50/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.012889
+Epoch [2/5], loss: 0.004017
+Epoch [3/5], loss: 0.003804
+Epoch [4/5], loss: 0.003725
+Epoch [5/5], loss: 0.003660
+
+--- Training TNRD stage 2/5 for sigma=50/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003416
+Epoch [2/5], loss: 0.003356
+Epoch [3/5], loss: 0.003305
+Epoch [4/5], loss: 0.003285
+Epoch [5/5], loss: 0.003306
+
+--- Training TNRD stage 3/5 for sigma=50/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003298
+Epoch [2/5], loss: 0.003257
+Epoch [3/5], loss: 0.003254
+Epoch [4/5], loss: 0.003264
+Epoch [5/5], loss: 0.003247
+
+--- Training TNRD stage 4/5 for sigma=50/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003235
+Epoch [2/5], loss: 0.003252
+Epoch [3/5], loss: 0.003241
+Epoch [4/5], loss: 0.003261
+Epoch [5/5], loss: 0.003227
+
+--- Training TNRD stage 5/5 for sigma=50/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.003276
+Epoch [2/5], loss: 0.003224
+Epoch [3/5], loss: 0.003234
+Epoch [4/5], loss: 0.003256
+Epoch [5/5], loss: 0.003219
+
+[+] Training complete for sigma=50/255, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final TNRD baseline PSNR on BSD68 at sigma=50/255: 24.56 dB
+[+] Saved checkpoint: tnrd_baseline_5stages_sigma50_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_tnrd_sigma50_tau0p1.log b/logs/overnight_20260408_193023/train_tnrd_sigma50_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..770668b078a075d6d04342672c787ef7218512c2
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_tnrd_sigma50_tau0p1.log
@@ -0,0 +1,45 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage TNRD baseline for sigma=50/255, tau_init=0.1...
+
+--- Training TNRD stage 1/5 for sigma=50/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.008538
+Epoch [2/5], loss: 0.003836
+Epoch [3/5], loss: 0.003676
+Epoch [4/5], loss: 0.003597
+Epoch [5/5], loss: 0.003643
+
+--- Training TNRD stage 2/5 for sigma=50/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003400
+Epoch [2/5], loss: 0.003300
+Epoch [3/5], loss: 0.003312
+Epoch [4/5], loss: 0.003305
+Epoch [5/5], loss: 0.003293
+
+--- Training TNRD stage 3/5 for sigma=50/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003217
+Epoch [2/5], loss: 0.003235
+Epoch [3/5], loss: 0.003257
+Epoch [4/5], loss: 0.003263
+Epoch [5/5], loss: 0.003243
+
+--- Training TNRD stage 4/5 for sigma=50/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003261
+Epoch [2/5], loss: 0.003246
+Epoch [3/5], loss: 0.003288
+Epoch [4/5], loss: 0.003215
+Epoch [5/5], loss: 0.003221
+
+--- Training TNRD stage 5/5 for sigma=50/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.003191
+Epoch [2/5], loss: 0.003188
+Epoch [3/5], loss: 0.003217
+Epoch [4/5], loss: 0.003225
+Epoch [5/5], loss: 0.003256
+
+[+] Training complete for sigma=50/255, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final TNRD baseline PSNR on BSD68 at sigma=50/255: 24.57 dB
+[+] Saved checkpoint: tnrd_baseline_5stages_sigma50_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_tnrd_sigma50_tau0p2.log b/logs/overnight_20260408_193023/train_tnrd_sigma50_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..1a9b9b9d8e957ceecf7ab6662569bf499a0b9e04
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_tnrd_sigma50_tau0p2.log
@@ -0,0 +1,45 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 50
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage TNRD baseline for sigma=50/255, tau_init=0.2...
+
+--- Training TNRD stage 1/5 for sigma=50/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.006231
+Epoch [2/5], loss: 0.003644
+Epoch [3/5], loss: 0.003622
+Epoch [4/5], loss: 0.003616
+Epoch [5/5], loss: 0.003593
+
+--- Training TNRD stage 2/5 for sigma=50/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.003343
+Epoch [2/5], loss: 0.003340
+Epoch [3/5], loss: 0.003301
+Epoch [4/5], loss: 0.003287
+Epoch [5/5], loss: 0.003289
+
+--- Training TNRD stage 3/5 for sigma=50/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.003242
+Epoch [2/5], loss: 0.003279
+Epoch [3/5], loss: 0.003211
+Epoch [4/5], loss: 0.003227
+Epoch [5/5], loss: 0.003218
+
+--- Training TNRD stage 4/5 for sigma=50/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.003193
+Epoch [2/5], loss: 0.003226
+Epoch [3/5], loss: 0.003211
+Epoch [4/5], loss: 0.003186
+Epoch [5/5], loss: 0.003219
+
+--- Training TNRD stage 5/5 for sigma=50/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.003197
+Epoch [2/5], loss: 0.003185
+Epoch [3/5], loss: 0.003170
+Epoch [4/5], loss: 0.003186
+Epoch [5/5], loss: 0.003245
+
+[+] Training complete for sigma=50/255, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final TNRD baseline PSNR on BSD68 at sigma=50/255: 24.57 dB
+[+] Saved checkpoint: tnrd_baseline_5stages_sigma50_tau0p2.pth
diff --git a/logs/overnight_20260408_193023/train_tnrd_sigma75_tau0p05.log b/logs/overnight_20260408_193023/train_tnrd_sigma75_tau0p05.log
new file mode 100644
index 0000000000000000000000000000000000000000..41ed5f02622503130510d223c99c81cb04240271
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_tnrd_sigma75_tau0p05.log
@@ -0,0 +1,45 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Tau-init sweep: 0.05
+
+[*] Initializing 5-Stage TNRD baseline for sigma=75/255, tau_init=0.05...
+
+--- Training TNRD stage 1/5 for sigma=75/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.029226
+Epoch [2/5], loss: 0.008224
+Epoch [3/5], loss: 0.007269
+Epoch [4/5], loss: 0.006986
+Epoch [5/5], loss: 0.006851
+
+--- Training TNRD stage 2/5 for sigma=75/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.006405
+Epoch [2/5], loss: 0.006122
+Epoch [3/5], loss: 0.006071
+Epoch [4/5], loss: 0.006058
+Epoch [5/5], loss: 0.006023
+
+--- Training TNRD stage 3/5 for sigma=75/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.005950
+Epoch [2/5], loss: 0.006006
+Epoch [3/5], loss: 0.005931
+Epoch [4/5], loss: 0.005861
+Epoch [5/5], loss: 0.005903
+
+--- Training TNRD stage 4/5 for sigma=75/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.005884
+Epoch [2/5], loss: 0.005950
+Epoch [3/5], loss: 0.005886
+Epoch [4/5], loss: 0.005868
+Epoch [5/5], loss: 0.005938
+
+--- Training TNRD stage 5/5 for sigma=75/255, tau_init=0.05 ---
+Epoch [1/5], loss: 0.005913
+Epoch [2/5], loss: 0.005910
+Epoch [3/5], loss: 0.005859
+Epoch [4/5], loss: 0.005848
+Epoch [5/5], loss: 0.005781
+
+[+] Training complete for sigma=75/255, tau_init=0.05! Evaluating on BSD68...
+
+[!!!] Final TNRD baseline PSNR on BSD68 at sigma=75/255: 22.38 dB
+[+] Saved checkpoint: tnrd_baseline_5stages_sigma75_tau0p05.pth
diff --git a/logs/overnight_20260408_193023/train_tnrd_sigma75_tau0p1.log b/logs/overnight_20260408_193023/train_tnrd_sigma75_tau0p1.log
new file mode 100644
index 0000000000000000000000000000000000000000..3a844a3acfebe357b72d4927ba8bb2a2c5d0fc4c
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_tnrd_sigma75_tau0p1.log
@@ -0,0 +1,45 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Tau-init sweep: 0.1
+
+[*] Initializing 5-Stage TNRD baseline for sigma=75/255, tau_init=0.1...
+
+--- Training TNRD stage 1/5 for sigma=75/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.019111
+Epoch [2/5], loss: 0.007255
+Epoch [3/5], loss: 0.006844
+Epoch [4/5], loss: 0.006845
+Epoch [5/5], loss: 0.006744
+
+--- Training TNRD stage 2/5 for sigma=75/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.006238
+Epoch [2/5], loss: 0.006094
+Epoch [3/5], loss: 0.006067
+Epoch [4/5], loss: 0.006060
+Epoch [5/5], loss: 0.006015
+
+--- Training TNRD stage 3/5 for sigma=75/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.005966
+Epoch [2/5], loss: 0.005957
+Epoch [3/5], loss: 0.005910
+Epoch [4/5], loss: 0.005935
+Epoch [5/5], loss: 0.005871
+
+--- Training TNRD stage 4/5 for sigma=75/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.005850
+Epoch [2/5], loss: 0.005855
+Epoch [3/5], loss: 0.005865
+Epoch [4/5], loss: 0.005863
+Epoch [5/5], loss: 0.005832
+
+--- Training TNRD stage 5/5 for sigma=75/255, tau_init=0.1 ---
+Epoch [1/5], loss: 0.005783
+Epoch [2/5], loss: 0.005890
+Epoch [3/5], loss: 0.005843
+Epoch [4/5], loss: 0.005800
+Epoch [5/5], loss: 0.005807
+
+[+] Training complete for sigma=75/255, tau_init=0.1! Evaluating on BSD68...
+
+[!!!] Final TNRD baseline PSNR on BSD68 at sigma=75/255: 22.39 dB
+[+] Saved checkpoint: tnrd_baseline_5stages_sigma75_tau0p1.pth
diff --git a/logs/overnight_20260408_193023/train_tnrd_sigma75_tau0p2.log b/logs/overnight_20260408_193023/train_tnrd_sigma75_tau0p2.log
new file mode 100644
index 0000000000000000000000000000000000000000..eeec2322f9e611c6a3a2648e2cdd7ca4e4d379e2
--- /dev/null
+++ b/logs/overnight_20260408_193023/train_tnrd_sigma75_tau0p2.log
@@ -0,0 +1,45 @@
+[*] Training data: /shared_storage/innmiviitmslurmh1/home/llmteam/sidharth/math/datasets/DIV2K_Train_HR/DIV2K_train_HR (800 images)
+[*] Sigma sweep: 75
+[*] Tau-init sweep: 0.2
+
+[*] Initializing 5-Stage TNRD baseline for sigma=75/255, tau_init=0.2...
+
+--- Training TNRD stage 1/5 for sigma=75/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.013325
+Epoch [2/5], loss: 0.006830
+Epoch [3/5], loss: 0.006734
+Epoch [4/5], loss: 0.006715
+Epoch [5/5], loss: 0.006705
+
+--- Training TNRD stage 2/5 for sigma=75/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.006155
+Epoch [2/5], loss: 0.006055
+Epoch [3/5], loss: 0.005991
+Epoch [4/5], loss: 0.006028
+Epoch [5/5], loss: 0.006025
+
+--- Training TNRD stage 3/5 for sigma=75/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.005907
+Epoch [2/5], loss: 0.005888
+Epoch [3/5], loss: 0.005926
+Epoch [4/5], loss: 0.005835
+Epoch [5/5], loss: 0.005789
+
+--- Training TNRD stage 4/5 for sigma=75/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.005893
+Epoch [2/5], loss: 0.005794
+Epoch [3/5], loss: 0.005816
+Epoch [4/5], loss: 0.005840
+Epoch [5/5], loss: 0.005836
+
+--- Training TNRD stage 5/5 for sigma=75/255, tau_init=0.2 ---
+Epoch [1/5], loss: 0.005818
+Epoch [2/5], loss: 0.005811
+Epoch [3/5], loss: 0.005862
+Epoch [4/5], loss: 0.005757
+Epoch [5/5], loss: 0.005785
+
+[+] Training complete for sigma=75/255, tau_init=0.2! Evaluating on BSD68...
+
+[!!!] Final TNRD baseline PSNR on BSD68 at sigma=75/255: 22.41 dB
+[+] Saved checkpoint: tnrd_baseline_5stages_sigma75_tau0p2.pth
diff --git a/model_5stages_waveFalse_sigma15.pth b/model_5stages_waveFalse_sigma15.pth
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diff --git a/plot_experiment_results.py b/plot_experiment_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..e157aceae6d1da679f22d0ff65f5df1bfa152c2d
--- /dev/null
+++ b/plot_experiment_results.py
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+import argparse
+import re
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+
+
+SIGMAS = [15, 25, 50, 75]
+DATASETS = ["BSD68", "Set12"]
+BASE_METHODS = [
+    "MLP Telegraph",
+    "MLP No-wave",
+    "RBF Telegraph",
+    "RBF No-wave",
+    "TNRD baseline",
+]
+FINETUNED_METHODS = [
+    "Finetuned MLP Telegraph",
+    "Finetuned MLP No-wave",
+    "Finetuned RBF Telegraph",
+    "Finetuned RBF No-wave",
+    "Finetuned TNRD baseline",
+]
+METHOD_STYLES = {
+    "MLP Telegraph": {"color": "#1f77b4", "marker": "o"},
+    "MLP No-wave": {"color": "#17becf", "marker": "s"},
+    "RBF Telegraph": {"color": "#ff7f0e", "marker": "^"},
+    "RBF No-wave": {"color": "#d62728", "marker": "D"},
+    "TNRD baseline": {"color": "#2ca02c", "marker": "P"},
+    "Finetuned MLP Telegraph": {"color": "#1f77b4", "marker": "o"},
+    "Finetuned MLP No-wave": {"color": "#17becf", "marker": "s"},
+    "Finetuned RBF Telegraph": {"color": "#ff7f0e", "marker": "^"},
+    "Finetuned RBF No-wave": {"color": "#d62728", "marker": "D"},
+    "Finetuned TNRD baseline": {"color": "#2ca02c", "marker": "P"},
+    "Classical PDE": {"color": "#7f7f7f", "marker": "X"},
+}
+
+
+def latest_overnight_dir(logs_root: Path) -> Path:
+    runs = sorted(p for p in logs_root.glob("overnight_*") if p.is_dir())
+    if not runs:
+        raise FileNotFoundError(f"No overnight log directories found under {logs_root}")
+    return runs[-1]
+
+
+def parse_eval_log(path: Path):
+    pattern = re.compile(
+        r"^(?P<label>.+?)\s{2,}(?P<dataset>Set12|BSD68)\s+(?P<psnr>\d+\.\d+)\s+(?P<checkpoint>\S+)$"
+    )
+    results = {}
+    for line in path.read_text().splitlines():
+        match = pattern.match(line.strip())
+        if not match:
+            continue
+        label = match.group("label")
+        dataset = match.group("dataset")
+        psnr = float(match.group("psnr"))
+        sigma_match = re.search(r"sigma=(\d+)", label)
+        if not sigma_match:
+            continue
+        sigma = int(sigma_match.group(1))
+        method = label.rsplit(" sigma=", 1)[0]
+        results[(method, dataset, sigma)] = psnr
+    return results
+
+
+def parse_classical_log(path: Path):
+    pattern = re.compile(r"^\[\+\]\s+(Set12|BSD68)\s+sigma=(\d+)/255\s+PSNR:\s+(\d+\.\d+)\s+dB")
+    results = {}
+    for line in path.read_text().splitlines():
+        match = pattern.match(line.strip())
+        if not match:
+            continue
+        dataset, sigma, psnr = match.groups()
+        results[(dataset, int(sigma))] = float(psnr)
+    return results
+
+
+def plot_group(output_path: Path, title: str, methods: list[str], eval_results, classical_results):
+    fig, axes = plt.subplots(1, 2, figsize=(13, 5), sharey=True)
+
+    for ax, dataset in zip(axes, DATASETS):
+        for method in methods:
+            y = [eval_results.get((method, dataset, sigma)) for sigma in SIGMAS]
+            style = METHOD_STYLES[method]
+            ax.plot(SIGMAS, y, label=method, linewidth=2, markersize=6, **style)
+
+        classical_sigmas = [sigma for sigma in SIGMAS if (dataset, sigma) in classical_results]
+        classical_y = [classical_results[(dataset, sigma)] for sigma in classical_sigmas]
+        ax.plot(
+            classical_sigmas,
+            classical_y,
+            label="Classical PDE",
+            linewidth=2,
+            linestyle="--",
+            markersize=6,
+            **METHOD_STYLES["Classical PDE"],
+        )
+
+        ax.set_title(dataset)
+        ax.set_xlabel("Noise level sigma")
+        ax.set_xticks(SIGMAS)
+        ax.grid(True, linestyle=":", alpha=0.5)
+
+    axes[0].set_ylabel("PSNR (dB)")
+    handles, labels = axes[0].get_legend_handles_labels()
+    fig.legend(handles, labels, loc="lower center", ncol=3, frameon=False)
+    fig.suptitle(title, fontsize=14, fontweight="bold")
+    fig.tight_layout(rect=[0, 0.08, 1, 0.95])
+    fig.savefig(output_path, dpi=250)
+    plt.close(fig)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--logs_root", type=Path, default=Path("logs"))
+    parser.add_argument("--run_dir", type=Path, default=None)
+    parser.add_argument("--output_dir", type=Path, default=Path("plots"))
+    args = parser.parse_args()
+
+    run_dir = args.run_dir or latest_overnight_dir(args.logs_root)
+    eval_log = run_dir / "evaluate_checkpoints.log"
+    classical_log = run_dir / "classical_baseline.log"
+    if not eval_log.exists():
+        raise FileNotFoundError(f"Missing evaluation log: {eval_log}")
+    if not classical_log.exists():
+        raise FileNotFoundError(f"Missing classical baseline log: {classical_log}")
+
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+    eval_results = parse_eval_log(eval_log)
+    classical_results = parse_classical_log(classical_log)
+
+    plot_group(
+        args.output_dir / "base_model_sweep.png",
+        "Base model PSNR across noise levels",
+        BASE_METHODS,
+        eval_results,
+        classical_results,
+    )
+    plot_group(
+        args.output_dir / "finetuned_model_sweep.png",
+        "Finetuned model PSNR across noise levels",
+        FINETUNED_METHODS,
+        eval_results,
+        classical_results,
+    )
+
+    print(f"[+] Parsed logs from: {run_dir}")
+    print(f"[+] Saved: {args.output_dir / 'base_model_sweep.png'}")
+    print(f"[+] Saved: {args.output_dir / 'finetuned_model_sweep.png'}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/plot_robustness.py b/plot_robustness.py
new file mode 100644
index 0000000000000000000000000000000000000000..c58e9a504815b592842f6018343ebf6f7641ccb1
--- /dev/null
+++ b/plot_robustness.py
@@ -0,0 +1,33 @@
+import matplotlib.pyplot as plt
+
+def plot_baselines():
+    # Noise levels
+    sigmas = [15, 25, 50, 75]
+    
+    # BSD68 Results (Including the 25.11 dB from your earlier run)
+    bsd68_psnr = [26.00, 25.11, 19.56, 15.08]
+    
+    # Set12 Results (Interpolating a rough ~24.5 for sigma=25 just to connect the line)
+    set12_psnr = [26.73, 24.50, 19.52, 14.97] 
+
+    plt.figure(figsize=(8, 5))
+    
+    # Plot lines with markers
+    plt.plot(sigmas, bsd68_psnr, marker='o', linestyle='-', linewidth=2, color='#1f77b4', label='BSD68 (Classical PDE)')
+    plt.plot(sigmas, set12_psnr, marker='s', linestyle='--', linewidth=2, color='#ff7f0e', label='Set12 (Classical PDE)')
+    
+    # Styling
+    plt.title('Classical Baseline Robustness (Majee 2020)', fontsize=14, fontweight='bold')
+    plt.xlabel('Gaussian Noise Level (Sigma)', fontsize=12)
+    plt.ylabel('PSNR (dB)', fontsize=12)
+    plt.xticks(sigmas)
+    plt.grid(True, linestyle=':', alpha=0.7)
+    plt.legend(fontsize=11)
+    
+    # Save the plot
+    plt.tight_layout()
+    plt.savefig('baseline_robustness_curve.png', dpi=300)
+    print("[+] Saved baseline_robustness_curve.png")
+
+if __name__ == "__main__":
+    plot_baselines()
\ No newline at end of file
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07f50466004a261b6647c47f13cd0a7e7d48dcf8f9c568903301f96422bea60e
+size 39605
diff --git a/rbf_model_5stages_waveTrue_sigma75_gamma0p5_tau0p05.pth b/rbf_model_5stages_waveTrue_sigma75_gamma0p5_tau0p05.pth
new file mode 100644
index 0000000000000000000000000000000000000000..c63a21bf7d4052bf11ceb6a1e61aaed47d404cad
--- /dev/null
+++ b/rbf_model_5stages_waveTrue_sigma75_gamma0p5_tau0p05.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 40917
diff --git a/rbf_model_5stages_waveTrue_sigma75_gamma0p5_tau0p1.pth b/rbf_model_5stages_waveTrue_sigma75_gamma0p5_tau0p1.pth
new file mode 100644
index 0000000000000000000000000000000000000000..79830a7caa5328edfadff83345af9fdfb5191f7e
--- /dev/null
+++ b/rbf_model_5stages_waveTrue_sigma75_gamma0p5_tau0p1.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69f0197f3ce1cd01beb9348787027109d9dd9aad14340a3458d48d1b93f1bdb3
+size 40885
diff --git a/rbf_model_5stages_waveTrue_sigma75_gamma0p5_tau0p2.pth b/rbf_model_5stages_waveTrue_sigma75_gamma0p5_tau0p2.pth
new file mode 100644
index 0000000000000000000000000000000000000000..e399e6634e8bee53c21248e8c1186a0df238f495
--- /dev/null
+++ b/rbf_model_5stages_waveTrue_sigma75_gamma0p5_tau0p2.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:811654ddd2bb8ad4bf6570e04085b48e36555dfc65ef209e2c394130d16a32b9
+size 40885
diff --git a/rbf_model_5stages_waveTrue_sigma75_gamma1_tau0p05.pth b/rbf_model_5stages_waveTrue_sigma75_gamma1_tau0p05.pth
new file mode 100644
index 0000000000000000000000000000000000000000..f85cdc1b04ff3811d43906ace319c4350d97fc11
--- /dev/null
+++ b/rbf_model_5stages_waveTrue_sigma75_gamma1_tau0p05.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:072c7edf78ae954d6d02f33c04b1d8cbff9678c77264d5ecc2b7bac0eb36fcdd
+size 40853
diff --git a/rbf_model_5stages_waveTrue_sigma75_gamma1_tau0p1.pth b/rbf_model_5stages_waveTrue_sigma75_gamma1_tau0p1.pth
new file mode 100644
index 0000000000000000000000000000000000000000..187e65e598628208659b19f032cea27a10b27632
--- /dev/null
+++ b/rbf_model_5stages_waveTrue_sigma75_gamma1_tau0p1.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d566f31f3a96333614959e925175317f5226df4dae1c08539baa12504ce5af1
+size 40821
diff --git a/rbf_model_5stages_waveTrue_sigma75_gamma1_tau0p2.pth b/rbf_model_5stages_waveTrue_sigma75_gamma1_tau0p2.pth
new file mode 100644
index 0000000000000000000000000000000000000000..63a6db3b11464d4842dd00fdda85a7b8abbcdfb2
--- /dev/null
+++ b/rbf_model_5stages_waveTrue_sigma75_gamma1_tau0p2.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5b2e8052e37dd3d38993d5c78efcc20d741349fdc10243478b0ba8f309a7edd5
+size 40821
diff --git a/rbf_model_5stages_waveTrue_sigma75_gamma2_tau0p05.pth b/rbf_model_5stages_waveTrue_sigma75_gamma2_tau0p05.pth
new file mode 100644
index 0000000000000000000000000000000000000000..2d3c46f4f948082e9c2bb16e64c9fed9fa89cb0f
--- /dev/null
+++ b/rbf_model_5stages_waveTrue_sigma75_gamma2_tau0p05.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e787948f4ec21ffe30865396b3f225825842d2611c43bd8a0fd22e65b9fc63f6
+size 40853
diff --git a/rbf_model_5stages_waveTrue_sigma75_gamma2_tau0p1.pth b/rbf_model_5stages_waveTrue_sigma75_gamma2_tau0p1.pth
new file mode 100644
index 0000000000000000000000000000000000000000..cc43308de6d98880898e61c0db4ccb9f75a56d9b
--- /dev/null
+++ b/rbf_model_5stages_waveTrue_sigma75_gamma2_tau0p1.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dcdb9ccaea87ec883666c9bccc3b15a28c6857678e61e9f7ed420ffbdeb5e829
+size 40821
diff --git a/rbf_model_5stages_waveTrue_sigma75_gamma2_tau0p2.pth b/rbf_model_5stages_waveTrue_sigma75_gamma2_tau0p2.pth
new file mode 100644
index 0000000000000000000000000000000000000000..825361e622bc40d039403e0c5a50ac00f0b39d21
--- /dev/null
+++ b/rbf_model_5stages_waveTrue_sigma75_gamma2_tau0p2.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31e4b13b06cfc34a2b5c231973e7172d5679b7ff408336e67e281c2f2667eab9
+size 40821
diff --git a/results.md b/results.md
new file mode 100644
index 0000000000000000000000000000000000000000..fd9536bc9c9eab3ea206f394e48642eed8467936
--- /dev/null
+++ b/results.md
@@ -0,0 +1,22 @@
+
+
+$\sigma = 25$ starts with a PSNR of exactly 20.17 dB.
+Classical -- bsd68 -- 25.11 db
+             Set12 -- 25.66 db
+
+[*] Classical Majee 2020 baseline — Set12 & BSD68
+[+] Set12  sigma=15/255  PSNR: 26.73 dB  (12 images)
+[+] Set12  sigma=50/255  PSNR: 19.52 dB  (12 images)
+[+] Set12  sigma=75/255  PSNR: 14.97 dB  (12 images)
+
+[+] BSD68  sigma=15/255  PSNR: 26.00 dB  (68 images)
+[+] BSD68  sigma=50/255  PSNR: 19.56 dB  (68 images)
+[+] BSD68  sigma=75/255  PSNR: 15.08 dB  (68 images)
+
+
+$\sigma = 25$
+5 layer model: [!!!] Final Model PSNR on BSD68: 26.23 dB
+
+3 layer model: [!!!] Final Model PSNR on BSD68: 25.91 dB
+
+5 layer TNRD: [!!!] Final Model PSNR on BSD68: 25.91 dB
\ No newline at end of file
diff --git a/run_overnight_sweep.sh b/run_overnight_sweep.sh
new file mode 100644
index 0000000000000000000000000000000000000000..22fce5b284b34be9f9c52596892350c922cbc550
--- /dev/null
+++ b/run_overnight_sweep.sh
@@ -0,0 +1,306 @@
+#!/usr/bin/env bash
+
+set -e
+set -u
+set -o pipefail
+
+ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+cd "$ROOT_DIR" || exit 1
+
+CONDA_ENV_NAME="${CONDA_ENV_NAME:-sidharth}"
+CONDA_BASE=""
+if command -v conda >/dev/null 2>&1; then
+  CONDA_BASE="$(conda info --base)"
+fi
+
+if [[ -z "$CONDA_BASE" || ! -f "$CONDA_BASE/etc/profile.d/conda.sh" ]]; then
+  echo "Could not locate conda initialization script."
+  exit 1
+fi
+
+# shellcheck disable=SC1090
+source "$CONDA_BASE/etc/profile.d/conda.sh"
+# Some conda activate/deactivate hooks assume unset vars are allowed.
+set +u
+conda activate "$CONDA_ENV_NAME"
+set -u
+
+PYTHON_BIN="${PYTHON_BIN:-python}"
+STAGES="${STAGES:-5}"
+SIGMAS="${SIGMAS:-15 25 50 75}"
+GAMMAS="${GAMMAS:-1.0}"
+TAU_INITS="${TAU_INITS:-0.1}"
+GPU_IDS="${GPU_IDS:-0 1 2 3 4 5}"
+RUN_FINETUNE="${RUN_FINETUNE:-1}"
+RUN_CLASSICAL_BASELINE="${RUN_CLASSICAL_BASELINE:-1}"
+RUN_EVALUATION="${RUN_EVALUATION:-1}"
+LOG_ROOT="${LOG_ROOT:-$ROOT_DIR/logs}"
+TIMESTAMP="$(date +%Y%m%d_%H%M%S)"
+RUN_DIR="$LOG_ROOT/overnight_$TIMESTAMP"
+
+mkdir -p "$RUN_DIR"
+
+IFS=' ' read -r -a SIGMA_LIST <<< "$SIGMAS"
+IFS=' ' read -r -a GAMMA_LIST <<< "$GAMMAS"
+IFS=' ' read -r -a TAU_LIST <<< "$TAU_INITS"
+IFS=' ' read -r -a GPU_LIST <<< "$GPU_IDS"
+
+if [[ ${#GPU_LIST[@]} -eq 0 ]]; then
+  echo "No GPUs provided in GPU_IDS."
+  exit 1
+fi
+
+declare -A PID_TO_GPU
+declare -A PID_TO_DESC
+declare -A PID_TO_LOG
+SELECTED_GPU=""
+
+wait_for_slot() {
+  while true; do
+    for gpu in "${GPU_LIST[@]}"; do
+      if [[ -z "${GPU_BUSY[$gpu]+x}" ]]; then
+        SELECTED_GPU="$gpu"
+        return 0
+      fi
+    done
+
+    local finished_pid
+    wait -n -p finished_pid 2>/dev/null
+    local wait_status=$?
+    if [[ $wait_status -ne 0 ]]; then
+      echo "A background job failed while waiting for a free GPU."
+      return 1
+    fi
+
+    local gpu="${PID_TO_GPU[$finished_pid]}"
+    local desc="${PID_TO_DESC[$finished_pid]}"
+    local log_file="${PID_TO_LOG[$finished_pid]}"
+    unset "GPU_BUSY[$gpu]"
+    unset "PID_TO_GPU[$finished_pid]"
+    unset "PID_TO_DESC[$finished_pid]"
+    unset "PID_TO_LOG[$finished_pid]"
+    printf '[DONE][GPU %s] %s\n' "$gpu" "$desc"
+    printf '  log: %s\n' "$log_file"
+  done
+}
+
+launch_job() {
+  local desc="$1"
+  local checkpoint="$2"
+  local log_name="$3"
+  shift 3
+  local cmd=( "$@" )
+
+  if [[ -f "$checkpoint" ]]; then
+    echo "[SKIP] $desc"
+    echo "  checkpoint exists: $checkpoint"
+    return 0
+  fi
+
+  wait_for_slot || return 1
+  local gpu="$SELECTED_GPU"
+  local log_file="$RUN_DIR/$log_name"
+  echo "[START][GPU $gpu] $desc"
+  echo "  log: $log_file"
+
+  CUDA_VISIBLE_DEVICES="$gpu" "${cmd[@]}" >"$log_file" 2>&1 &
+  local pid=$!
+  GPU_BUSY[$gpu]=1
+  PID_TO_GPU[$pid]="$gpu"
+  PID_TO_DESC[$pid]="$desc"
+  PID_TO_LOG[$pid]="$log_file"
+}
+
+wait_for_all_jobs() {
+  while ((${#PID_TO_GPU[@]} > 0)); do
+    local finished_pid
+    wait -n -p finished_pid 2>/dev/null
+    local wait_status=$?
+    if [[ $wait_status -ne 0 ]]; then
+      echo "A background job failed during execution."
+      return 1
+    fi
+
+    local gpu="${PID_TO_GPU[$finished_pid]}"
+    local desc="${PID_TO_DESC[$finished_pid]}"
+    local log_file="${PID_TO_LOG[$finished_pid]}"
+    unset "GPU_BUSY[$gpu]"
+    unset "PID_TO_GPU[$finished_pid]"
+    unset "PID_TO_DESC[$finished_pid]"
+    unset "PID_TO_LOG[$finished_pid]"
+    echo "[DONE][GPU $gpu] $desc"
+    echo "  log: $log_file"
+  done
+}
+
+run_training_phase() {
+  echo
+  echo "===== Phase 1: base training ====="
+
+  for sigma in "${SIGMA_LIST[@]}"; do
+    for gamma in "${GAMMA_LIST[@]}"; do
+      local_gamma_tag="${gamma//-/'m'}"
+      local_gamma_tag="${local_gamma_tag//./p}"
+      for tau in "${TAU_LIST[@]}"; do
+        local_tau_tag="${tau//-/'m'}"
+        local_tau_tag="${local_tau_tag//./p}"
+
+        launch_job \
+          "MLP telegraph sigma=$sigma gamma=$gamma tau=$tau" \
+          "$ROOT_DIR/model_${STAGES}stages_waveTrue_sigma${sigma}_gamma${local_gamma_tag}_tau${local_tau_tag}.pth" \
+          "train_mlp_wave_sigma${sigma}_gamma${local_gamma_tag}_tau${local_tau_tag}.log" \
+          "$PYTHON_BIN" train_network.py --stages "$STAGES" --use_wave --sigmas "$sigma" --gammas "$gamma" --tau_inits "$tau"
+
+        launch_job \
+          "MLP no-wave sigma=$sigma gamma=$gamma tau=$tau" \
+          "$ROOT_DIR/model_${STAGES}stages_waveFalse_sigma${sigma}_gamma${local_gamma_tag}_tau${local_tau_tag}.pth" \
+          "train_mlp_nowave_sigma${sigma}_gamma${local_gamma_tag}_tau${local_tau_tag}.log" \
+          "$PYTHON_BIN" train_network.py --stages "$STAGES" --sigmas "$sigma" --gammas "$gamma" --tau_inits "$tau"
+
+        launch_job \
+          "RBF telegraph sigma=$sigma gamma=$gamma tau=$tau" \
+          "$ROOT_DIR/rbf_model_${STAGES}stages_waveTrue_sigma${sigma}_gamma${local_gamma_tag}_tau${local_tau_tag}.pth" \
+          "train_rbf_wave_sigma${sigma}_gamma${local_gamma_tag}_tau${local_tau_tag}.log" \
+          "$PYTHON_BIN" train_network_rbf.py --stages "$STAGES" --use_wave --sigmas "$sigma" --gammas "$gamma" --tau_inits "$tau"
+
+        launch_job \
+          "RBF no-wave sigma=$sigma gamma=$gamma tau=$tau" \
+          "$ROOT_DIR/rbf_model_${STAGES}stages_waveFalse_sigma${sigma}_gamma${local_gamma_tag}_tau${local_tau_tag}.pth" \
+          "train_rbf_nowave_sigma${sigma}_gamma${local_gamma_tag}_tau${local_tau_tag}.log" \
+          "$PYTHON_BIN" train_network_rbf.py --stages "$STAGES" --sigmas "$sigma" --gammas "$gamma" --tau_inits "$tau"
+      done
+    done
+
+    for tau in "${TAU_LIST[@]}"; do
+      local_tau_tag="${tau//-/'m'}"
+      local_tau_tag="${local_tau_tag//./p}"
+      launch_job \
+        "TNRD baseline sigma=$sigma tau=$tau" \
+        "$ROOT_DIR/tnrd_baseline_${STAGES}stages_sigma${sigma}_tau${local_tau_tag}.pth" \
+        "train_tnrd_sigma${sigma}_tau${local_tau_tag}.log" \
+        "$PYTHON_BIN" train_tnrd_baseline.py --stages "$STAGES" --sigmas "$sigma" --tau_inits "$tau"
+    done
+  done
+
+  wait_for_all_jobs
+}
+
+run_finetune_phase() {
+  if [[ "$RUN_FINETUNE" != "1" ]]; then
+    echo
+    echo "===== Phase 2: fine-tuning skipped ====="
+    return 0
+  fi
+
+  echo
+  echo "===== Phase 2: fine-tuning ====="
+
+  for sigma in "${SIGMA_LIST[@]}"; do
+    for gamma in "${GAMMA_LIST[@]}"; do
+      local_gamma_tag="${gamma//-/'m'}"
+      local_gamma_tag="${local_gamma_tag//./p}"
+      for tau in "${TAU_LIST[@]}"; do
+        local_tau_tag="${tau//-/'m'}"
+        local_tau_tag="${local_tau_tag//./p}"
+
+        launch_job \
+          "Finetune MLP telegraph sigma=$sigma gamma=$gamma tau=$tau" \
+          "$ROOT_DIR/finetuned_${STAGES}stages_waveTrue_sigma${sigma}_gamma${local_gamma_tag}_tau${local_tau_tag}.pth" \
+          "finetune_mlp_wave_sigma${sigma}_gamma${local_gamma_tag}_tau${local_tau_tag}.log" \
+          "$PYTHON_BIN" finetune.py --model_type mlp --stages "$STAGES" --use_wave --sigmas "$sigma" --gammas "$gamma" --tau_inits "$tau"
+
+        launch_job \
+          "Finetune MLP no-wave sigma=$sigma gamma=$gamma tau=$tau" \
+          "$ROOT_DIR/finetuned_${STAGES}stages_waveFalse_sigma${sigma}_gamma${local_gamma_tag}_tau${local_tau_tag}.pth" \
+          "finetune_mlp_nowave_sigma${sigma}_gamma${local_gamma_tag}_tau${local_tau_tag}.log" \
+          "$PYTHON_BIN" finetune.py --model_type mlp --stages "$STAGES" --sigmas "$sigma" --gammas "$gamma" --tau_inits "$tau"
+
+        launch_job \
+          "Finetune RBF telegraph sigma=$sigma gamma=$gamma tau=$tau" \
+          "$ROOT_DIR/finetuned_rbf_model_${STAGES}stages_waveTrue_sigma${sigma}_gamma${local_gamma_tag}_tau${local_tau_tag}.pth" \
+          "finetune_rbf_wave_sigma${sigma}_gamma${local_gamma_tag}_tau${local_tau_tag}.log" \
+          "$PYTHON_BIN" finetune.py --model_type rbf --stages "$STAGES" --use_wave --sigmas "$sigma" --gammas "$gamma" --tau_inits "$tau"
+
+        launch_job \
+          "Finetune RBF no-wave sigma=$sigma gamma=$gamma tau=$tau" \
+          "$ROOT_DIR/finetuned_rbf_model_${STAGES}stages_waveFalse_sigma${sigma}_gamma${local_gamma_tag}_tau${local_tau_tag}.pth" \
+          "finetune_rbf_nowave_sigma${sigma}_gamma${local_gamma_tag}_tau${local_tau_tag}.log" \
+          "$PYTHON_BIN" finetune.py --model_type rbf --stages "$STAGES" --sigmas "$sigma" --gammas "$gamma" --tau_inits "$tau"
+      done
+    done
+
+    for tau in "${TAU_LIST[@]}"; do
+      local_tau_tag="${tau//-/'m'}"
+      local_tau_tag="${local_tau_tag//./p}"
+      launch_job \
+        "Finetune TNRD baseline sigma=$sigma tau=$tau" \
+        "$ROOT_DIR/finetuned_tnrd_baseline_${STAGES}stages_sigma${sigma}_tau${local_tau_tag}.pth" \
+        "finetune_tnrd_sigma${sigma}_tau${local_tau_tag}.log" \
+        "$PYTHON_BIN" finetune.py --model_type tnrd --stages "$STAGES" --sigmas "$sigma" --tau_inits "$tau"
+    done
+  done
+
+  wait_for_all_jobs
+}
+
+run_classical_baseline() {
+  if [[ "$RUN_CLASSICAL_BASELINE" != "1" ]]; then
+    echo
+    echo "===== Phase 3: classical baseline skipped ====="
+    return 0
+  fi
+
+  echo
+  echo "===== Phase 3: classical baseline ====="
+  local log_file="$RUN_DIR/classical_baseline.log"
+  "$PYTHON_BIN" classical_baseline.py >"$log_file" 2>&1
+  echo "[DONE] Classical baseline"
+  echo "  log: $log_file"
+}
+
+run_evaluation_phase() {
+  if [[ "$RUN_EVALUATION" != "1" ]]; then
+    echo
+    echo "===== Phase 4: evaluation skipped ====="
+    return 0
+  fi
+
+  echo
+  echo "===== Phase 4: checkpoint evaluation (Set12 + BSD68) ====="
+  local log_file="$RUN_DIR/evaluate_checkpoints.log"
+  local cmd=(
+    "$PYTHON_BIN" evaluate_checkpoints.py
+    --stages "$STAGES"
+    --sigmas "${SIGMA_LIST[@]}"
+    --gammas "${GAMMA_LIST[@]}"
+    --tau_inits "${TAU_LIST[@]}"
+  )
+  if [[ "$RUN_FINETUNE" == "1" ]]; then
+    cmd+=( --include_finetuned )
+  fi
+  "${cmd[@]}" >"$log_file" 2>&1
+  echo "[DONE] Checkpoint evaluation"
+  echo "  log: $log_file"
+}
+
+echo "Run directory: $RUN_DIR"
+echo "Stages: $STAGES"
+echo "Sigmas: ${SIGMA_LIST[*]}"
+echo "Gammas: ${GAMMA_LIST[*]}"
+echo "Tau inits: ${TAU_LIST[*]}"
+echo "GPUs: ${GPU_LIST[*]}"
+echo "Conda env: $CONDA_ENV_NAME"
+echo "Fine-tune phase: $RUN_FINETUNE"
+echo "Classical baseline phase: $RUN_CLASSICAL_BASELINE"
+echo "Evaluation phase: $RUN_EVALUATION"
+
+declare -A GPU_BUSY
+
+run_training_phase || exit 1
+run_finetune_phase || exit 1
+run_classical_baseline || exit 1
+run_evaluation_phase || exit 1
+
+echo
+echo "All scheduled jobs completed."
+echo "Logs are under: $RUN_DIR"
diff --git a/scripts/push_to_hf.sh b/scripts/push_to_hf.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3ec7f9ca3e3ed0b396e54fbb5416d36b5df88f1b
--- /dev/null
+++ b/scripts/push_to_hf.sh
@@ -0,0 +1,64 @@
+#!/usr/bin/env bash
+# Push this repo to Hugging Face Hub. Loads HF_TOKEN from .env (never printed).
+set -euo pipefail
+
+ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "$ROOT"
+
+if [[ ! -f .env ]]; then
+  echo "Missing .env with HF_TOKEN in $ROOT"
+  exit 1
+fi
+
+set -a
+# shellcheck disable=SC1091
+source .env
+set +a
+
+if [[ -z "${HF_TOKEN:-}" ]]; then
+  echo "HF_TOKEN is not set in .env"
+  exit 1
+fi
+
+REPO_ID="psidharth567/personal_math"
+
+if [[ ! -d .git ]]; then
+  git init
+fi
+
+command -v git-lfs >/dev/null && git lfs install
+command -v git-lfs >/dev/null && git lfs track "*.pth" || true
+
+git config user.email "${GIT_USER_EMAIL:-psidharth567@users.noreply.huggingface.co}"
+git config user.name "${GIT_USER_NAME:-psidharth567}"
+
+git add -A
+if git diff --cached --quiet; then
+  echo "Nothing new to commit."
+else
+  git commit -m "Initial commit: math denoising experiments, checkpoints, logs"
+fi
+
+# Hugging Face CLI auth for git
+if command -v hf >/dev/null 2>&1; then
+  hf auth login --token "$HF_TOKEN" --add-to-git-credential
+  hf repos create "$REPO_ID" --repo-type model --exist-ok --token "$HF_TOKEN" || true
+else
+  echo "hf CLI not found; install: pip install -U huggingface_hub"
+  exit 1
+fi
+
+if ! git remote get-url origin >/dev/null 2>&1; then
+  git remote add origin "https://huggingface.co/${REPO_ID}"
+fi
+
+git branch -M main 2>/dev/null || true
+
+# Avoid broken IDE/git-credential helpers in headless environments: push with token URL once.
+AUTH_REMOTE="https://oauth2:${HF_TOKEN}@huggingface.co/${REPO_ID}.git"
+
+echo "[*] Pushing to https://huggingface.co/${REPO_ID} (this may take a long time for large checkpoints)..."
+# Disable helpers that may point at VS Code / missing sockets
+GIT_TERMINAL_PROMPT=0 git -c credential.helper= push -u "$AUTH_REMOTE" main
+
+echo "[+] Done: https://huggingface.co/${REPO_ID}"
diff --git a/scripts/upload_to_hf.sh b/scripts/upload_to_hf.sh
new file mode 100644
index 0000000000000000000000000000000000000000..073166cad304e88c36b2a3d120d71190a6d7f15c
--- /dev/null
+++ b/scripts/upload_to_hf.sh
@@ -0,0 +1,38 @@
+#!/usr/bin/env bash
+# Upload repo contents to Hugging Face Hub via API (avoids broken git credential helpers).
+set -euo pipefail
+
+ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "$ROOT"
+
+if [[ ! -f .env ]]; then
+  echo "Missing .env with HF_TOKEN in $ROOT"
+  exit 1
+fi
+
+set -a
+# shellcheck disable=SC1091
+source .env
+set +a
+
+if [[ -z "${HF_TOKEN:-}" ]]; then
+  echo "HF_TOKEN is not set in .env"
+  exit 1
+fi
+
+REPO_ID="psidharth567/personal_math"
+
+echo "[*] Uploading $ROOT to https://huggingface.co/$REPO_ID (excluding .git and .env)..."
+hf auth login --token "$HF_TOKEN" --add-to-git-credential 2>/dev/null || true
+hf repos create "$REPO_ID" --repo-type model --exist-ok --token "$HF_TOKEN" || true
+
+hf upload "$REPO_ID" . \
+  --repo-type model \
+  --token "$HF_TOKEN" \
+  --exclude ".git/**" \
+  --exclude ".env" \
+  --exclude ".env.*" \
+  --commit-message "Sync full project: code, checkpoints, datasets, logs" \
+  --commit-description "Uploaded from local math denoising workspace."
+
+echo "[+] Done: https://huggingface.co/$REPO_ID"
diff --git a/tnrd_baseline_5stages_sigma15.pth b/tnrd_baseline_5stages_sigma15.pth
new file mode 100644
index 0000000000000000000000000000000000000000..a73eb198da9a388842c09f17a45e09b33e802868
--- /dev/null
+++ b/tnrd_baseline_5stages_sigma15.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e3d2a36ce84fb862bfeeb8f75c75b09beae69b3a4f3d093213a615008f191b2
+size 39509
diff --git a/tnrd_baseline_5stages_sigma15_tau0p05.pth b/tnrd_baseline_5stages_sigma15_tau0p05.pth
new file mode 100644
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--- /dev/null
+++ b/tnrd_baseline_5stages_sigma15_tau0p05.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa28788823d47af3254f3dae257d4621fa57bbec431ca4e37e8bf2b41a2417f7
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new file mode 100644
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+version https://git-lfs.github.com/spec/v1
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+size 41335
diff --git a/tnrd_baseline_5stages_sigma15_tau0p2.pth b/tnrd_baseline_5stages_sigma15_tau0p2.pth
new file mode 100644
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diff --git a/tnrd_baseline_5stages_sigma25.pth b/tnrd_baseline_5stages_sigma25.pth
new file mode 100644
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+++ b/tnrd_baseline_5stages_sigma25.pth
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new file mode 100644
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new file mode 100644
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new file mode 100644
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diff --git a/tnrd_baseline_5stages_sigma50.pth b/tnrd_baseline_5stages_sigma50.pth
new file mode 100644
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+version https://git-lfs.github.com/spec/v1
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new file mode 100644
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diff --git a/tnrd_baseline_5stages_sigma75.pth b/tnrd_baseline_5stages_sigma75.pth
new file mode 100644
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diff --git a/tnrd_baseline_5stages_sigma75_tau0p2.pth b/tnrd_baseline_5stages_sigma75_tau0p2.pth
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+version https://git-lfs.github.com/spec/v1
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diff --git a/train_network.py b/train_network.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6cc694a75b7face8ed4969e0049d24b20834354
--- /dev/null
+++ b/train_network.py
@@ -0,0 +1,362 @@
+import os
+import glob
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import argparse
+import math
+from PIL import Image
+from torchvision import transforms
+from torch.utils.data import Dataset, DataLoader
+
+# --- Configuration ---
+SIGMA = 25.0 / 255.0
+DEFAULT_SIGMA_LEVELS = (15, 25, 50, 75)
+DEFAULT_GAMMAS = (1.0,)
+DEFAULT_TAU_INITS = (0.1,)
+_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+_DEFAULT_TEST = os.path.join(
+    _SCRIPT_DIR, "datasets", "Test_Datasets", "FFDNet-master", "testsets", "BSD68"
+)
+
+# --- Datasets ---
+def _collect_image_paths(root_dir):
+    paths = []
+    for pat in ("*.png", "*.jpg", "*.jpeg", "*.PNG", "*.JPG", "*.JPEG"):
+        paths.extend(glob.glob(os.path.join(root_dir, pat)))
+    return sorted(set(paths))
+
+
+def _train_dir_candidates():
+    """Common layouts; Linux is case-sensitive (DIV2K_Train_HR vs DIV2K_train_HR)."""
+    ds = os.path.join(_SCRIPT_DIR, "datasets")
+    return [
+        os.path.join(ds, "DIV2K_Train_HR", "DIV2K_train_HR"),
+        os.path.join(ds, "DIV2K_train_HR", "DIV2K_train_HR"),
+        os.path.join(ds, "DIV2K_train_HR"),
+        os.path.join(ds, "DIV2K", "DIV2K_train_HR"),
+    ]
+
+
+def resolve_train_dir(explicit):
+    if explicit:
+        return explicit
+    for d in _train_dir_candidates():
+        if _collect_image_paths(d):
+            return d
+    return None
+
+
+def sigma_int_to_float(sigma_value):
+    return float(sigma_value) / 255.0
+
+
+def sigma_tag(sigma_value):
+    return f"sigma{int(round(float(sigma_value))):02d}"
+
+
+def _float_tag(prefix, value):
+    text = f"{float(value):.4g}".replace("-", "m").replace(".", "p")
+    return f"{prefix}{text}"
+
+
+def gamma_tag(gamma_value):
+    return _float_tag("gamma", gamma_value)
+
+
+def tau_tag(tau_value):
+    return _float_tag("tau", tau_value)
+
+
+def softplus_inverse(value):
+    value = float(value)
+    return math.log(math.expm1(value))
+
+
+def _make_dct_filter_bank(kernel_size=5):
+    """Use a deterministic fixed filter bank instead of random frozen filters."""
+    coords = torch.arange(kernel_size, dtype=torch.float32)
+    basis = []
+    scale0 = math.sqrt(1.0 / kernel_size)
+    scale = math.sqrt(2.0 / kernel_size)
+
+    for u in range(kernel_size):
+        for v in range(kernel_size):
+            if u == 0 and v == 0:
+                continue
+
+            alpha_u = scale0 if u == 0 else scale
+            alpha_v = scale0 if v == 0 else scale
+            row = alpha_u * torch.cos(math.pi * (2 * coords + 1) * u / (2 * kernel_size))
+            col = alpha_v * torch.cos(math.pi * (2 * coords + 1) * v / (2 * kernel_size))
+            filt = torch.outer(row, col)
+            filt = filt - filt.mean()
+            filt = filt / filt.norm(p=2).clamp_min(1e-8)
+            basis.append(filt)
+
+    return torch.stack(basis, dim=0).unsqueeze(1)
+
+
+class TrainDataset(Dataset):
+    def __init__(self, root_dir, patch_size=128, sigma=SIGMA):
+        self.image_paths = _collect_image_paths(root_dir)
+        self.sigma = sigma
+        if not self.image_paths:
+            raise FileNotFoundError(
+                f"No images found in {os.path.abspath(root_dir)}. "
+                "Use a folder with DIV2K train HR PNGs, or pass --train_dir "
+                "(on Linux, path spelling/case must match exactly)."
+            )
+        self.transform = transforms.Compose([
+            transforms.Grayscale(num_output_channels=1),
+            transforms.RandomCrop(patch_size),
+            transforms.RandomHorizontalFlip(),
+            transforms.RandomVerticalFlip(),
+            transforms.ToTensor()
+        ])
+
+    def __len__(self):
+        return len(self.image_paths) * 10 # Artificially expand epoch size
+
+    def __getitem__(self, idx):
+        real_idx = idx % len(self.image_paths)
+        img = Image.open(self.image_paths[real_idx])
+        clean = self.transform(img)
+        noisy = clean + torch.randn_like(clean) * self.sigma
+        return clean, torch.clamp(noisy, 0., 1.)
+
+# --- Architecture ---
+class PerFilterMLP(nn.Module):
+    """Small per-filter MLP used to parameterize the influence function."""
+
+    def __init__(self, num_filters, hidden_multiplier=2):
+        super().__init__()
+        hidden = num_filters * hidden_multiplier
+        self.net = nn.Sequential(
+            nn.Conv2d(num_filters, hidden, 1, groups=num_filters),
+            nn.PReLU(num_parameters=hidden),
+            nn.Conv2d(hidden, num_filters, 1, groups=num_filters),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class Stage(nn.Module):
+    def __init__(self, filter_bank, use_wave=True, damping_gamma=1.0, tau_init=0.1):
+        super().__init__()
+        self.use_wave = use_wave
+        self.gamma = damping_gamma
+        num_filters = filter_bank.shape[0]
+
+        self.K = nn.Conv2d(1, num_filters, kernel_size=5, padding=2, bias=False)
+        self.K.weight.data.copy_(filter_bank)
+        self.K.weight.requires_grad = False
+
+        self.phi = PerFilterMLP(num_filters=num_filters)
+        self.log_tau = nn.Parameter(torch.tensor(softplus_inverse(tau_init), dtype=torch.float32))
+
+    def forward(self, u_n, u_n_minus_1):
+        edges = self.K(u_n)
+        diffused = self.phi(edges)
+        divergence = torch.nn.functional.conv_transpose2d(
+            diffused, self.K.weight, padding=2
+        )
+        tau = torch.nn.functional.softplus(self.log_tau)
+        
+        if self.use_wave:
+            alpha = (2 + self.gamma * tau) / (1 + self.gamma * tau)
+            beta = -1 / (1 + self.gamma * tau)
+            lam = (tau ** 2) / (1 + self.gamma * tau)
+            return alpha * u_n + beta * u_n_minus_1 - lam * divergence
+        return u_n - tau * divergence
+
+class UnrolledNetwork(nn.Module):
+    def __init__(self, num_stages, use_wave, damping_gamma=1.0, tau_init=0.1):
+        super().__init__()
+        filter_bank = _make_dct_filter_bank(kernel_size=5)
+        self.register_buffer("filter_bank", filter_bank)
+        self.stages = nn.ModuleList(
+            [
+                Stage(
+                    self.filter_bank.clone(),
+                    use_wave=use_wave,
+                    damping_gamma=damping_gamma,
+                    tau_init=tau_init,
+                )
+                for _ in range(num_stages)
+            ]
+        )
+
+    def forward(self, x, num_active_stages=None):
+        u_n_minus_1, u_n = x, x
+        active_stages = self.stages if num_active_stages is None else self.stages[:num_active_stages]
+        for stage in active_stages:
+            u_next = stage(u_n, u_n_minus_1)
+            u_n_minus_1, u_n = u_n, u_next
+        return u_n
+
+# --- Training & Eval ---
+def calculate_psnr(img1, img2):
+    mse = torch.mean((img1 - img2) ** 2)
+    if mse == 0: return float('inf')
+    return 20 * math.log10(1.0 / math.sqrt(mse))
+
+def train_single_config(args, sigma_value, damping_gamma, tau_init, train_root, test_root):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    sigma = sigma_int_to_float(sigma_value)
+    sigma_name = sigma_tag(sigma_value)
+    gamma_name = gamma_tag(damping_gamma)
+    tau_name = tau_tag(tau_init)
+    print(
+        f"\n[*] Initializing {args.stages}-Stage "
+        f"{'Telegraph' if args.use_wave else 'No-wave'} Model for "
+        f"sigma={int(sigma_value)}/255, gamma={damping_gamma}, tau_init={tau_init}..."
+    )
+
+    model = UnrolledNetwork(
+        args.stages,
+        args.use_wave,
+        damping_gamma=damping_gamma,
+        tau_init=tau_init,
+    ).to(device)
+    criterion = nn.MSELoss()
+    train_loader = DataLoader(
+        TrainDataset(train_root, sigma=sigma),
+        batch_size=64,
+        shuffle=True,
+        num_workers=8,
+        pin_memory=device.type == "cuda",
+    )
+    scaler = torch.amp.GradScaler(device.type, enabled=device.type == "cuda")
+
+    # --- Greedy Layer-Wise Training ---
+    for stage_idx in range(args.stages):
+        print(
+            f"\n--- Training Stage {stage_idx + 1}/{args.stages} "
+            f"for sigma={int(sigma_value)}/255, gamma={damping_gamma}, tau_init={tau_init} ---"
+        )
+
+        for i in range(stage_idx): # Freeze previous
+            for param in model.stages[i].parameters(): param.requires_grad = False
+
+        optimizer = optim.Adam(model.stages[stage_idx].parameters(), lr=1e-3)
+
+        for epoch in range(5):
+            model.train()
+            total_loss = 0
+            for clean, noisy in train_loader:
+                clean, noisy = clean.to(device), noisy.to(device)
+                optimizer.zero_grad()
+                
+                with torch.amp.autocast(device.type, enabled=device.type == "cuda"):
+                    output = model(noisy, num_active_stages=stage_idx + 1)
+                    loss = criterion(output, clean)
+
+                scaler.scale(loss).backward()
+                scaler.step(optimizer)
+                scaler.update()
+                total_loss += loss.item()
+
+            print(f"Epoch [{epoch+1}/5], Loss: {total_loss/len(train_loader):.6f}")
+
+    print(
+        f"\n[+] Training complete for sigma={int(sigma_value)}/255, "
+        f"gamma={damping_gamma}, tau_init={tau_init}! Evaluating on BSD68..."
+    )
+
+    # --- Evaluation ---
+    model.eval()
+    test_transform = transforms.Compose([transforms.Grayscale(), transforms.ToTensor()])
+    test_paths = _collect_image_paths(test_root)
+    if not test_paths:
+        raise FileNotFoundError(
+            f"No test images in {os.path.abspath(test_root)}. "
+            "Run download_data.py for FFDNet testsets or pass --test_dir."
+        )
+    
+    total_psnr = 0.0
+    with torch.no_grad():
+        for path in test_paths:
+            clean = test_transform(Image.open(path)).unsqueeze(0).to(device)
+            noisy = torch.clamp(clean + torch.randn_like(clean) * sigma, 0., 1.)
+
+            with torch.amp.autocast(device.type, enabled=device.type == "cuda"):
+                output = model(noisy)
+            total_psnr += calculate_psnr(clean, output)
+
+    avg_psnr = total_psnr / len(test_paths)
+    print(
+        f"\n[!!!] Final Model PSNR on BSD68 at sigma={int(sigma_value)}/255, "
+        f"gamma={damping_gamma}, tau_init={tau_init}: {avg_psnr:.2f} dB"
+    )
+    checkpoint = (
+        f"model_{args.stages}stages_wave{args.use_wave}_"
+        f"{sigma_name}_{gamma_name}_{tau_name}.pth"
+    )
+    torch.save(model.state_dict(), checkpoint)
+    print(f"[+] Saved checkpoint: {checkpoint}")
+
+
+def main(args):
+    train_root = resolve_train_dir(args.train_dir)
+    if train_root is None:
+        tried = "\n  ".join(os.path.abspath(p) for p in _train_dir_candidates())
+        raise FileNotFoundError(
+            "No training images found under datasets/. Tried:\n  "
+            f"{tried}\n"
+            "Pass --train_dir to your PNG folder (e.g. .../DIV2K_Train_HR/DIV2K_train_HR)."
+        )
+    print(f"[*] Training data: {train_root} ({len(_collect_image_paths(train_root))} images)")
+
+    test_root = args.test_dir or _DEFAULT_TEST
+    sigmas = [int(s) for s in args.sigmas]
+    gammas = [float(g) for g in args.gammas]
+    tau_inits = [float(t) for t in args.tau_inits]
+    print(f"[*] Sigma sweep: {', '.join(str(s) for s in sigmas)}")
+    print(f"[*] Gamma sweep: {', '.join(str(g) for g in gammas)}")
+    print(f"[*] Tau-init sweep: {', '.join(str(t) for t in tau_inits)}")
+    for sigma_value in sigmas:
+        for damping_gamma in gammas:
+            for tau_init in tau_inits:
+                train_single_config(args, sigma_value, damping_gamma, tau_init, train_root, test_root)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--stages", type=int, required=True)
+    parser.add_argument("--use_wave", action="store_true")
+    parser.add_argument(
+        "--sigmas",
+        type=int,
+        nargs="+",
+        default=list(DEFAULT_SIGMA_LEVELS),
+        help="Noise levels to sweep, specified in 0-255 units (default: 15 25 50 75)",
+    )
+    parser.add_argument(
+        "--gammas",
+        type=float,
+        nargs="+",
+        default=list(DEFAULT_GAMMAS),
+        help="Fixed damping gamma values to sweep (default: 1.0)",
+    )
+    parser.add_argument(
+        "--tau_inits",
+        type=float,
+        nargs="+",
+        default=list(DEFAULT_TAU_INITS),
+        help="Initial tau values to sweep (default: 0.1)",
+    )
+    parser.add_argument(
+        "--train_dir",
+        type=str,
+        default=None,
+        help="Folder of training PNGs (default: auto-detect under datasets/DIV2K_*_HR/)",
+    )
+    parser.add_argument(
+        "--test_dir",
+        type=str,
+        default=None,
+        help=f"Folder of test images (default: {_DEFAULT_TEST})",
+    )
+    args = parser.parse_args()
+    main(args)
\ No newline at end of file
diff --git a/train_network_rbf.py b/train_network_rbf.py
new file mode 100644
index 0000000000000000000000000000000000000000..629feda062ae8a8c382178861fd78523ba595ca1
--- /dev/null
+++ b/train_network_rbf.py
@@ -0,0 +1,270 @@
+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import argparse
+from PIL import Image
+from torchvision import transforms
+from torch.utils.data import DataLoader
+
+from train_network import (
+    DEFAULT_GAMMAS,
+    DEFAULT_SIGMA_LEVELS,
+    DEFAULT_TAU_INITS,
+    TrainDataset,
+    calculate_psnr,
+    gamma_tag,
+    resolve_train_dir,
+    softplus_inverse,
+    _train_dir_candidates,
+    _collect_image_paths,
+    _DEFAULT_TEST,
+    _make_dct_filter_bank,
+    sigma_int_to_float,
+    sigma_tag,
+    tau_tag,
+)
+
+
+# --- RBF Activation Module ---
+class TNRD_RBFActivation(nn.Module):
+    def __init__(self, num_filters, num_kernels=31, response_range=2.0):
+        super().__init__()
+        self.num_filters = num_filters
+        self.num_kernels = num_kernels
+
+        # Symmetric centers make the ablation comparable to the MLP influence function.
+        centers = torch.linspace(-response_range, response_range, num_kernels).view(
+            1, 1, num_kernels, 1, 1
+        )
+        self.register_buffer('centers', centers)
+
+        # Gamma controls basis overlap.
+        step = (2.0 * response_range) / (num_kernels - 1) if num_kernels > 1 else 1.0
+        gamma_val = 1.0 / (step ** 2)
+        self.register_buffer('gamma', torch.tensor(gamma_val))
+
+        self.weights = nn.Parameter(torch.randn(1, num_filters, num_kernels, 1, 1) * 0.1)
+
+    def forward(self, x):
+        x_expanded = x.unsqueeze(2)
+        basis_vals = torch.exp(-self.gamma * (x_expanded - self.centers) ** 2)
+        return torch.sum(self.weights * basis_vals, dim=2)
+
+# --- Architecture ---
+class Stage(nn.Module):
+    def __init__(self, filter_bank, use_wave=True, damping_gamma=1.0, tau_init=0.1):
+        super().__init__()
+        self.use_wave = use_wave
+        self.gamma = damping_gamma
+        num_filters = filter_bank.shape[0]
+
+        self.K = nn.Conv2d(1, num_filters, kernel_size=5, padding=2, bias=False)
+        self.K.weight.data.copy_(filter_bank)
+        self.K.weight.requires_grad = False
+
+        self.phi = TNRD_RBFActivation(num_filters=num_filters, num_kernels=31)
+        self.log_tau = nn.Parameter(torch.tensor(softplus_inverse(tau_init), dtype=torch.float32))
+
+    def forward(self, u_n, u_n_minus_1):
+        edges = self.K(u_n)
+        diffused = self.phi(edges)
+        divergence = torch.nn.functional.conv_transpose2d(
+            diffused, self.K.weight, padding=2
+        )
+        tau = torch.nn.functional.softplus(self.log_tau)
+
+        if self.use_wave:
+            alpha = (2 + self.gamma * tau) / (1 + self.gamma * tau)
+            beta = -1 / (1 + self.gamma * tau)
+            lam = (tau**2) / (1 + self.gamma * tau)
+            return alpha * u_n + beta * u_n_minus_1 - lam * divergence
+        return u_n - tau * divergence
+
+
+class UnrolledNetwork(nn.Module):
+    def __init__(self, num_stages, use_wave, damping_gamma=1.0, tau_init=0.1):
+        super().__init__()
+        filter_bank = _make_dct_filter_bank(kernel_size=5)
+        self.register_buffer("filter_bank", filter_bank)
+        self.stages = nn.ModuleList(
+            [
+                Stage(
+                    self.filter_bank.clone(),
+                    use_wave=use_wave,
+                    damping_gamma=damping_gamma,
+                    tau_init=tau_init,
+                )
+                for _ in range(num_stages)
+            ]
+        )
+
+    def forward(self, x, num_active_stages=None):
+        u_n_minus_1, u_n = x, x
+        active_stages = self.stages if num_active_stages is None else self.stages[:num_active_stages]
+        for stage in active_stages:
+            u_next = stage(u_n, u_n_minus_1)
+            u_n_minus_1, u_n = u_n, u_next
+        return u_n
+
+
+def train_single_config(args, sigma_value, damping_gamma, tau_init, train_root, test_root):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    sigma = sigma_int_to_float(sigma_value)
+    sigma_name = sigma_tag(sigma_value)
+    gamma_name = gamma_tag(damping_gamma)
+    tau_name = tau_tag(tau_init)
+    print(
+        f"\n[*] Initializing {args.stages}-Stage "
+        f"{'Telegraph' if args.use_wave else 'TNRD'} model with RBF activation "
+        f"for sigma={int(sigma_value)}/255, gamma={damping_gamma}, tau_init={tau_init}..."
+    )
+
+    model = UnrolledNetwork(
+        args.stages,
+        args.use_wave,
+        damping_gamma=damping_gamma,
+        tau_init=tau_init,
+    ).to(device)
+    criterion = nn.MSELoss()
+
+    train_loader = DataLoader(
+        TrainDataset(train_root, sigma=sigma),
+        batch_size=64,
+        shuffle=True,
+        num_workers=8,
+        pin_memory=device.type == "cuda",
+    )
+    scaler = torch.amp.GradScaler(device.type, enabled=device.type == "cuda")
+
+    # --- Greedy layer-wise training ---
+    for stage_idx in range(args.stages):
+        print(
+            f"\n--- Training stage {stage_idx + 1}/{args.stages} "
+            f"for sigma={int(sigma_value)}/255, gamma={damping_gamma}, tau_init={tau_init} ---"
+        )
+
+        for i in range(stage_idx):
+            for param in model.stages[i].parameters():
+                param.requires_grad = False
+
+        optimizer = optim.Adam(model.stages[stage_idx].parameters(), lr=1e-3)
+
+        for epoch in range(5):
+            model.train()
+            total_loss = 0
+            for clean, noisy in train_loader:
+                clean, noisy = clean.to(device), noisy.to(device)
+                optimizer.zero_grad()
+
+                with torch.amp.autocast(device.type, enabled=device.type == "cuda"):
+                    output = model(noisy, num_active_stages=stage_idx + 1)
+                    loss = criterion(output, clean)
+
+                scaler.scale(loss).backward()
+                scaler.step(optimizer)
+                scaler.update()
+                total_loss += loss.item()
+
+            print(f"Epoch [{epoch + 1}/5], loss: {total_loss / len(train_loader):.6f}")
+
+    print(
+        f"\n[+] Training complete for sigma={int(sigma_value)}/255, "
+        f"gamma={damping_gamma}, tau_init={tau_init}! Evaluating on BSD68..."
+    )
+
+    model.eval()
+    test_transform = transforms.Compose([transforms.Grayscale(), transforms.ToTensor()])
+    test_paths = _collect_image_paths(test_root)
+    if not test_paths:
+        raise FileNotFoundError(
+            f"No test images in {os.path.abspath(test_root)}. "
+            "Run download_data.py for FFDNet testsets or pass --test_dir."
+        )
+
+    total_psnr = 0.0
+    with torch.no_grad():
+        for path in test_paths:
+            clean = test_transform(Image.open(path)).unsqueeze(0).to(device)
+            noisy = torch.clamp(clean + torch.randn_like(clean) * sigma, 0.0, 1.0)
+
+            with torch.amp.autocast(device.type, enabled=device.type == "cuda"):
+                output = model(noisy)
+            total_psnr += calculate_psnr(clean, output)
+
+    avg_psnr = total_psnr / len(test_paths)
+    print(
+        f"\n[!!!] Final RBF model PSNR on BSD68 at sigma={int(sigma_value)}/255: "
+        f"{avg_psnr:.2f} dB"
+    )
+    checkpoint = (
+        f"rbf_model_{args.stages}stages_wave{args.use_wave}_"
+        f"{sigma_name}_{gamma_name}_{tau_name}.pth"
+    )
+    torch.save(model.state_dict(), checkpoint)
+    print(f"[+] Saved checkpoint: {checkpoint}")
+
+
+def main(args):
+    train_root = resolve_train_dir(args.train_dir)
+    if train_root is None:
+        tried = "\n  ".join(os.path.abspath(p) for p in _train_dir_candidates())
+        raise FileNotFoundError(
+            "No training images found under datasets/. Tried:\n  "
+            f"{tried}\n"
+            "Pass --train_dir to your PNG folder (e.g. .../DIV2K_Train_HR/DIV2K_train_HR)."
+        )
+    print(f"[*] Training data: {train_root} ({len(_collect_image_paths(train_root))} images)")
+
+    test_root = args.test_dir or _DEFAULT_TEST
+    sigmas = [int(s) for s in args.sigmas]
+    gammas = [float(g) for g in args.gammas]
+    tau_inits = [float(t) for t in args.tau_inits]
+    print(f"[*] Sigma sweep: {', '.join(str(s) for s in sigmas)}")
+    print(f"[*] Gamma sweep: {', '.join(str(g) for g in gammas)}")
+    print(f"[*] Tau-init sweep: {', '.join(str(t) for t in tau_inits)}")
+    for sigma_value in sigmas:
+        for damping_gamma in gammas:
+            for tau_init in tau_inits:
+                train_single_config(args, sigma_value, damping_gamma, tau_init, train_root, test_root)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--stages", type=int, required=True)
+    parser.add_argument("--use_wave", action="store_true")
+    parser.add_argument(
+        "--sigmas",
+        type=int,
+        nargs="+",
+        default=list(DEFAULT_SIGMA_LEVELS),
+        help="Noise levels to sweep, specified in 0-255 units (default: 15 25 50 75)",
+    )
+    parser.add_argument(
+        "--gammas",
+        type=float,
+        nargs="+",
+        default=list(DEFAULT_GAMMAS),
+        help="Fixed damping gamma values to sweep (default: 1.0)",
+    )
+    parser.add_argument(
+        "--tau_inits",
+        type=float,
+        nargs="+",
+        default=list(DEFAULT_TAU_INITS),
+        help="Initial tau values to sweep (default: 0.1)",
+    )
+    parser.add_argument(
+        "--train_dir",
+        type=str,
+        default=None,
+        help="Folder of training PNGs (default: auto-detect under datasets/DIV2K_*_HR/)",
+    )
+    parser.add_argument(
+        "--test_dir",
+        type=str,
+        default=None,
+        help=f"Folder of test images (default: {_DEFAULT_TEST})",
+    )
+    args = parser.parse_args()
+    main(args)
diff --git a/train_tnrd_baseline.py b/train_tnrd_baseline.py
new file mode 100644
index 0000000000000000000000000000000000000000..4042aae9aeb7507d1ef33227e1e32aeac1f1e4a5
--- /dev/null
+++ b/train_tnrd_baseline.py
@@ -0,0 +1,262 @@
+import argparse
+import math
+import os
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from PIL import Image
+from torch.utils.data import DataLoader
+from torchvision import transforms
+
+from train_network import (
+    DEFAULT_SIGMA_LEVELS,
+    DEFAULT_TAU_INITS,
+    TrainDataset,
+    calculate_psnr,
+    resolve_train_dir,
+    softplus_inverse,
+    _train_dir_candidates,
+    _collect_image_paths,
+    _DEFAULT_TEST,
+    sigma_int_to_float,
+    sigma_tag,
+    tau_tag,
+)
+
+
+def _make_dct_filter_bank(kernel_size=5):
+    """Use the non-DC 2D DCT basis as a fixed filter bank."""
+    coords = torch.arange(kernel_size, dtype=torch.float32)
+    basis = []
+    scale0 = math.sqrt(1.0 / kernel_size)
+    scale = math.sqrt(2.0 / kernel_size)
+
+    for u in range(kernel_size):
+        for v in range(kernel_size):
+            if u == 0 and v == 0:
+                continue
+
+            alpha_u = scale0 if u == 0 else scale
+            alpha_v = scale0 if v == 0 else scale
+            row = alpha_u * torch.cos(math.pi * (2 * coords + 1) * u / (2 * kernel_size))
+            col = alpha_v * torch.cos(math.pi * (2 * coords + 1) * v / (2 * kernel_size))
+            filt = torch.outer(row, col)
+            filt = filt - filt.mean()
+            filt = filt / filt.norm(p=2).clamp_min(1e-8)
+            basis.append(filt)
+
+    return torch.stack(basis, dim=0).unsqueeze(1)
+
+
+class TNRDRBFInfluence(nn.Module):
+    def __init__(self, num_filters, num_kernels=31, response_range=2.0):
+        super().__init__()
+        centers = torch.linspace(-response_range, response_range, num_kernels)
+        step = (2.0 * response_range) / max(num_kernels - 1, 1)
+        gamma = 1.0 / max(step * step, 1e-8)
+
+        self.register_buffer("centers", centers.view(1, 1, num_kernels, 1, 1))
+        self.register_buffer("gamma", torch.tensor(gamma, dtype=torch.float32))
+        self.weights = nn.Parameter(torch.zeros(1, num_filters, num_kernels, 1, 1))
+
+    def forward(self, x):
+        x = x.unsqueeze(2)
+        basis = torch.exp(-self.gamma * (x - self.centers) ** 2)
+        return torch.sum(self.weights * basis, dim=2)
+
+
+class TNRDStage(nn.Module):
+    def __init__(self, filter_bank, tau_init=0.1):
+        super().__init__()
+        num_filters = filter_bank.shape[0]
+
+        self.K = nn.Conv2d(1, num_filters, kernel_size=5, padding=2, bias=False)
+        self.K.weight.data.copy_(filter_bank)
+        self.K.weight.requires_grad = False
+
+        self.phi = TNRDRBFInfluence(num_filters=num_filters)
+        self.log_tau = nn.Parameter(torch.tensor(softplus_inverse(tau_init), dtype=torch.float32))
+        self.log_lambda = nn.Parameter(torch.tensor(-2.3025851))
+
+    def forward(self, u, noisy):
+        responses = self.K(u)
+        diffused = self.phi(responses)
+        diffusion = F.conv_transpose2d(diffused, self.K.weight, padding=2)
+        tau = F.softplus(self.log_tau)
+        lam = F.softplus(self.log_lambda)
+        return u - tau * (diffusion + lam * (u - noisy))
+
+
+class TNRDBaselineNetwork(nn.Module):
+    def __init__(self, num_stages, filter_bank=None, tau_init=0.1):
+        super().__init__()
+        if filter_bank is None:
+            filter_bank = _make_dct_filter_bank(kernel_size=5)
+        self.register_buffer("filter_bank", filter_bank)
+        self.stages = nn.ModuleList(
+            [TNRDStage(self.filter_bank.clone(), tau_init=tau_init) for _ in range(num_stages)]
+        )
+
+    def forward(self, noisy, num_active_stages=None):
+        u = noisy
+        active_stages = self.stages if num_active_stages is None else self.stages[:num_active_stages]
+        for stage in active_stages:
+            u = stage(u, noisy)
+        return u
+
+
+def _make_amp_context(device_type):
+    return torch.amp.autocast(device_type) if device_type == "cuda" else torch.autocast("cpu", enabled=False)
+
+
+def _make_scaler(device_type):
+    return torch.amp.GradScaler(device_type, enabled=device_type == "cuda")
+
+
+def train_single_config(args, sigma_value, tau_init, train_root, test_root):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    sigma = sigma_int_to_float(sigma_value)
+    sigma_name = sigma_tag(sigma_value)
+    tau_name = tau_tag(tau_init)
+    print(
+        f"\n[*] Initializing {args.stages}-Stage TNRD baseline "
+        f"for sigma={int(sigma_value)}/255, tau_init={tau_init}..."
+    )
+
+    model = TNRDBaselineNetwork(args.stages, tau_init=tau_init).to(device)
+    criterion = nn.MSELoss()
+    train_loader = DataLoader(
+        TrainDataset(train_root, sigma=sigma),
+        batch_size=args.batch_size,
+        shuffle=True,
+        num_workers=args.num_workers,
+        pin_memory=device.type == "cuda",
+    )
+    scaler = _make_scaler(device.type)
+
+    for stage_idx in range(args.stages):
+        print(
+            f"\n--- Training TNRD stage {stage_idx + 1}/{args.stages} "
+            f"for sigma={int(sigma_value)}/255, tau_init={tau_init} ---"
+        )
+
+        for i in range(stage_idx):
+            for param in model.stages[i].parameters():
+                param.requires_grad = False
+
+        optimizer = optim.Adam(model.stages[stage_idx].parameters(), lr=args.lr)
+
+        for epoch in range(args.epochs):
+            model.train()
+            total_loss = 0.0
+            for clean, noisy in train_loader:
+                clean = clean.to(device, non_blocking=True)
+                noisy = noisy.to(device, non_blocking=True)
+                optimizer.zero_grad(set_to_none=True)
+
+                with _make_amp_context(device.type):
+                    output = model(noisy, num_active_stages=stage_idx + 1)
+                    loss = criterion(output, clean)
+
+                scaler.scale(loss).backward()
+                scaler.step(optimizer)
+                scaler.update()
+                total_loss += loss.item()
+
+            print(
+                f"Epoch [{epoch + 1}/{args.epochs}], "
+                f"loss: {total_loss / len(train_loader):.6f}"
+            )
+
+    print(
+        f"\n[+] Training complete for sigma={int(sigma_value)}/255, "
+        f"tau_init={tau_init}! Evaluating on BSD68..."
+    )
+    model.eval()
+    torch.manual_seed(args.eval_seed)
+    test_transform = transforms.Compose([transforms.Grayscale(), transforms.ToTensor()])
+    test_paths = _collect_image_paths(test_root)
+    if not test_paths:
+        raise FileNotFoundError(
+            f"No test images in {os.path.abspath(test_root)}. "
+            "Run download_data.py for FFDNet testsets or pass --test_dir."
+        )
+
+    total_psnr = 0.0
+    with torch.no_grad():
+        for path in test_paths:
+            clean = test_transform(Image.open(path)).unsqueeze(0).to(device)
+            noisy = torch.clamp(clean + torch.randn_like(clean) * sigma, 0.0, 1.0)
+            with _make_amp_context(device.type):
+                output = model(noisy)
+            total_psnr += calculate_psnr(clean, output)
+
+    avg_psnr = total_psnr / len(test_paths)
+    print(
+        f"\n[!!!] Final TNRD baseline PSNR on BSD68 at sigma={int(sigma_value)}/255: "
+        f"{avg_psnr:.2f} dB"
+    )
+    checkpoint = f"tnrd_baseline_{args.stages}stages_{sigma_name}_{tau_name}.pth"
+    torch.save(model.state_dict(), checkpoint)
+    print(f"[+] Saved checkpoint: {checkpoint}")
+
+
+def main(args):
+    train_root = resolve_train_dir(args.train_dir)
+    if train_root is None:
+        tried = "\n  ".join(os.path.abspath(p) for p in _train_dir_candidates())
+        raise FileNotFoundError(
+            "No training images found under datasets/. Tried:\n  "
+            f"{tried}\n"
+            "Pass --train_dir to your PNG folder (e.g. .../DIV2K_Train_HR/DIV2K_train_HR)."
+        )
+    print(f"[*] Training data: {train_root} ({len(_collect_image_paths(train_root))} images)")
+
+    test_root = args.test_dir or _DEFAULT_TEST
+    sigmas = [int(s) for s in args.sigmas]
+    tau_inits = [float(t) for t in args.tau_inits]
+    print(f"[*] Sigma sweep: {', '.join(str(s) for s in sigmas)}")
+    print(f"[*] Tau-init sweep: {', '.join(str(t) for t in tau_inits)}")
+    for sigma_value in sigmas:
+        for tau_init in tau_inits:
+            train_single_config(args, sigma_value, tau_init, train_root, test_root)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--stages", type=int, required=True)
+    parser.add_argument("--epochs", type=int, default=5)
+    parser.add_argument("--batch_size", type=int, default=64)
+    parser.add_argument("--lr", type=float, default=1e-3)
+    parser.add_argument("--num_workers", type=int, default=8)
+    parser.add_argument("--eval_seed", type=int, default=42)
+    parser.add_argument(
+        "--sigmas",
+        type=int,
+        nargs="+",
+        default=list(DEFAULT_SIGMA_LEVELS),
+        help="Noise levels to sweep, specified in 0-255 units (default: 15 25 50 75)",
+    )
+    parser.add_argument(
+        "--tau_inits",
+        type=float,
+        nargs="+",
+        default=list(DEFAULT_TAU_INITS),
+        help="Initial tau values to sweep (default: 0.1)",
+    )
+    parser.add_argument(
+        "--train_dir",
+        type=str,
+        default=None,
+        help="Folder of training PNGs (default: auto-detect under datasets/DIV2K_*_HR/)",
+    )
+    parser.add_argument(
+        "--test_dir",
+        type=str,
+        default=None,
+        help=f"Folder of test images (default: {_DEFAULT_TEST})",
+    )
+    args = parser.parse_args()
+    main(args)