README.md

---
license: gpl-3.0
tags:
- img2img
- denoiser
- image
---

# denoise_large_v1

denoise_large_v1 is an image denoiser made for images that have a high/medium amount of noise.

It performs slightly better than [denoise_medium_v1](https://huggingface.co/vericudebuget/denoise_medium_v1) on most images and can reconstruct a higher level of detail.


## Model Details



### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** [ConvoLite AI]
- **Funded by:** [VDB]
- **Model type:** [img2img]
- **License:** [gpl-3.0]


## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
For comercial and noncomercial use.

### Direct Use
For CPU, use the code below:
``` python
import os
import torch
import torch.nn as nn
from PIL import Image
from torchvision.transforms import ToTensor
import numpy as np
from concurrent.futures import ThreadPoolExecutor

class DenoisingModel(nn.Module):
    def __init__(self):
        super(DenoisingModel, self).__init__()
        self.enc1 = nn.Sequential(
            nn.Conv2d(3, 64, 3, padding=1),
            nn.ReLU(),
            nn.Conv2d(64, 64, 3, padding=1),
            nn.ReLU()
        )
        self.pool1 = nn.MaxPool2d(2, 2)
        
        self.up1 = nn.ConvTranspose2d(64, 64, 2, stride=2)
        self.dec1 = nn.Sequential(
            nn.Conv2d(64, 64, 3, padding=1),
            nn.ReLU(),
            nn.Conv2d(64, 3, 3, padding=1)
        )

    def forward(self, x):
        e1 = self.enc1(x)
        p1 = self.pool1(e1)
        u1 = self.up1(p1)
        d1 = self.dec1(u1)
        return d1

def denoise_patch(model, patch):
    transform = ToTensor()
    input_patch = transform(patch).unsqueeze(0)
    
    with torch.no_grad():
        output_patch = model(input_patch)
    
    denoised_patch = output_patch.squeeze(0).permute(1, 2, 0).numpy() * 255
    denoised_patch = np.clip(denoised_patch, 0, 255).astype(np.uint8)
    
    original_patch = np.array(patch)
    very_bright_mask = original_patch > 240
    bright_mask = (original_patch > 220) & (original_patch <= 240)
    
    denoised_patch[very_bright_mask] = original_patch[very_bright_mask]
    
    blend_factor = 0.7
    denoised_patch[bright_mask] = (
        blend_factor * original_patch[bright_mask] +
        (1 - blend_factor) * denoised_patch[bright_mask]
    )
    
    return denoised_patch

def denoise_image(image_path, model_path, patch_size=256, num_threads=4, overlap=32):
    model = DenoisingModel()
    checkpoint = torch.load(model_path, map_location=torch.device('cpu'))
    model.load_state_dict(checkpoint['model_state_dict'])
    model.eval()
    
    # Load and get original image dimensions
    image = Image.open(image_path).convert("RGB")
    width, height = image.size
    
    # Calculate padding needed
    pad_right = patch_size - (width % patch_size) if width % patch_size != 0 else 0
    pad_bottom = patch_size - (height % patch_size) if height % patch_size != 0 else 0
    
    # Add padding with reflection instead of zeros
    padded_width = width + pad_right
    padded_height = height + pad_bottom
    
    # Create padded image using reflection padding
    padded_image = Image.new("RGB", (padded_width, padded_height))
    padded_image.paste(image, (0, 0))
    
    # Fill right border with reflected content
    if pad_right > 0:
        right_border = image.crop((width - pad_right, 0, width, height))
        padded_image.paste(right_border.transpose(Image.FLIP_LEFT_RIGHT), (width, 0))
    
    # Fill bottom border with reflected content
    if pad_bottom > 0:
        bottom_border = image.crop((0, height - pad_bottom, width, height))
        padded_image.paste(bottom_border.transpose(Image.FLIP_TOP_BOTTOM), (0, height))
    
    # Fill corner if needed
    if pad_right > 0 and pad_bottom > 0:
        corner = image.crop((width - pad_right, height - pad_bottom, width, height))
        padded_image.paste(corner.transpose(Image.FLIP_LEFT_RIGHT).transpose(Image.FLIP_TOP_BOTTOM), 
                          (width, height))
    
    # Generate patches with positions
    patches = []
    positions = []
    for i in range(0, padded_height, patch_size - overlap):
        for j in range(0, padded_width, patch_size - overlap):
            patch = padded_image.crop((j, i, min(j + patch_size, padded_width), min(i + patch_size, padded_height)))
            patches.append(patch)
            positions.append((i, j))
    
    # Process patches in parallel
    with ThreadPoolExecutor(max_workers=num_threads) as executor:
        denoised_patches = list(executor.map(lambda p: denoise_patch(model, p), patches))
    
    # Initialize output arrays
    denoised_image = np.zeros((padded_height, padded_width, 3), dtype=np.float32)
    weight_map = np.zeros((padded_height, padded_width), dtype=np.float32)
    
    # Create smooth blending weights
    for (i, j), denoised_patch in zip(positions, denoised_patches):
        patch_height, patch_width, _ = denoised_patch.shape
        patch_weights = np.ones((patch_height, patch_width), dtype=np.float32)
        if i > 0:
            patch_weights[:overlap, :] *= np.linspace(0, 1, overlap)[:, np.newaxis]
        if j > 0:
            patch_weights[:, :overlap] *= np.linspace(0, 1, overlap)[np.newaxis, :]
        if i + patch_height < padded_height:
            patch_weights[-overlap:, :] *= np.linspace(1, 0, overlap)[:, np.newaxis]
        if j + patch_width < padded_width:
            patch_weights[:, -overlap:] *= np.linspace(1, 0, overlap)[np.newaxis, :]
        
        # Clip the patch values to prevent very bright pixels
        denoised_patch = np.clip(denoised_patch, 0, 255)
        
        denoised_image[i:i + patch_height, j:j + patch_width] += (
            denoised_patch * patch_weights[:, :, np.newaxis]
        )
        weight_map[i:i + patch_height, j:j + patch_width] += patch_weights
    
    # Normalize by weights
    mask = weight_map > 0
    denoised_image[mask] = denoised_image[mask] / weight_map[mask, np.newaxis]
    
    # Crop to original size
    denoised_image = denoised_image[:height, :width]
    denoised_image = np.clip(denoised_image, 0, 255).astype(np.uint8)
    
    # Save the result
    denoised_image_path = os.path.splitext(image_path)[0] + "_denoised.png"
    print(f"Saving denoised image to {denoised_image_path}")
    
    Image.fromarray(denoised_image).save(denoised_image_path)

if __name__ == "__main__":
    image_path = input("Enter the path of the image: ")
    model_path = r"path/to/model.pkl"
    denoise_image(image_path, model_path, num_threads=12)
    print("Denoising completed.")  # Use the number of threads your processor has.)
```


### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

If the image does not have a high level of noise, it is not recommended to use this model, as it will produce less than ideal results.


## Training Details

This model was trained on a single Nvidia T4 GPU for around 2 hours and 30 minutes.

### Training Data

Around 13 GB of publicly available images under the Creative Commons license.

#### Speed

With an AMD Ryzen 5 5500 it can denoise a 2k image in approx. 2 seconds using multithreading. Still have not tested it out with CUDA, but it's probably faster.



#### Hardware


| Specifications | Minimum | Recommended |
|----------|----------|----------|
| CPU | Intel Core i7-2700K or something else that can run Python | AMD Ryzen 5 5500 |
| RAM | 4 GB | 16 GB |
| GPU | not needed | Nvidia GTX 1660 Ti |


#### Software

Python



## Model Card Authors 

Vericu de Buget


## Model Card Contact

[convolite@europe.com](mailto:convolite@europe.com)
[ConvoLite](https://convolite.github.io/selector.html)