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downsample_wb.py

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+import os
+import cv2
+import glob
+
+def downsample_image(image_path):
+    # 读取图像
+    img = cv2.imread(image_path)
+    if img is None:
+        print(f"Error reading image: {image_path}")
+        return None
+    
+    # 获取原始尺寸
+    h, w = img.shape[:2]
+    
+    # 下采样到一半大小
+    resized = cv2.resize(img, (w//2, h//2), interpolation=cv2.INTER_CUBIC)
+    
+    return resized
+
+def process_folders():
+    # 定义需要处理的文件夹
+    folders = [
+        'DIV2K_train_EDGE_disturbed',
+        'DIV2K_train_HR',
+        'DIV2K_train_LR_bicubic/X1'  # LR_bicubic中的图像在X1子文件夹中
+    ]
+    
+    # 需要处理的图片名称
+    target_images = ['wb1.jpg', 'wb2.jpg', 'wb3.jpg']
+    
+    for folder in folders:
+        print(f"\n处理文件夹: {folder}")
+        
+        # 处理每个目标图片
+        for img_name in target_images:
+            img_path = os.path.join(folder, img_name)
+            if os.path.exists(img_path):
+                print(f"处理图片: {img_path}")
+                # 下采样图片
+                resized = downsample_image(img_path)
+                if resized is not None:
+                    # 直接覆盖原图片
+                    cv2.imwrite(img_path, resized)
+                    print(f"已完成下采样: {img_path}")
+            else:
+                print(f"找不到图片: {img_path}")
+
+if __name__ == '__main__':
+    process_folders() 

experiment/log.txt

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+smgarn_1：
+使用4个视频，正常图像，EWT模型，训练集520，测试集50
+python main.py --scale 1 --patch_size 64 --save smgarn --ext sep_reset --save_results
+
+smagrn_2:
+对参考图像增加高斯噪声和高斯模糊，其余不变。在div2k.py中31行，hardcode了噪声图像文件夹信息
+python main.py --scale 1 --patch_size 64 --save smgarn --ext sep_reset --save_results

experiment/smgarn/config.txt

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+2025-01-06-05:02:54
+
+debug: False
+template: .
+n_threads: 6
+cpu: False
+n_GPUs: 2
+seed: 1
+dir_data: ../dataset
+dir_demo: ../test
+data_train: ['DIV2K']
+data_test: ['DIV2K']
+data_range: 1-520/521-570
+ext: sep_reset
+scale: [1]
+patch_size: 64
+rgb_range: 255
+n_colors: 3
+chop: False
+no_augment: False
+model: EWT
+act: relu
+pre_train: 
+extend: .
+n_resblocks: 16
+n_feats: 64
+res_scale: 1
+shift_mean: True
+dilation: False
+precision: single
+G0: 64
+RDNkSize: 3
+RDNconfig: B
+n_resgroups: 10
+reduction: 16
+reset: False
+test_every: 1000
+epochs: 300
+batch_size: 16
+split_batch: 1
+self_ensemble: False
+test_only: False
+gan_k: 1
+lr: 0.0001
+decay: 100
+gamma: 0.5
+optimizer: ADAM
+momentum: 0.9
+betas: (0.9, 0.999)
+epsilon: 1e-08
+weight_decay: 0
+gclip: 0
+loss: 1*L1
+skip_threshold: 100000000.0
+save: smgarn
+load: 
+resume: 0
+save_models: False
+print_every: 100
+save_results: True
+save_gt: False
+
+2025-01-06-05:03:11
+
+debug: False
+template: .
+n_threads: 6
+cpu: False
+n_GPUs: 2
+seed: 1
+dir_data: ../dataset
+dir_demo: ../test
+data_train: ['DIV2K']
+data_test: ['DIV2K']
+data_range: 1-520/521-570
+ext: sep_reset
+scale: [1]
+patch_size: 64
+rgb_range: 255
+n_colors: 3
+chop: False
+no_augment: False
+model: EWT
+act: relu
+pre_train: 
+extend: .
+n_resblocks: 16
+n_feats: 64
+res_scale: 1
+shift_mean: True
+dilation: False
+precision: single
+G0: 64
+RDNkSize: 3
+RDNconfig: B
+n_resgroups: 10
+reduction: 16
+reset: False
+test_every: 1000
+epochs: 300
+batch_size: 16
+split_batch: 1
+self_ensemble: False
+test_only: False
+gan_k: 1
+lr: 0.0001
+decay: 100
+gamma: 0.5
+optimizer: ADAM
+momentum: 0.9
+betas: (0.9, 0.999)
+epsilon: 1e-08
+weight_decay: 0
+gclip: 0
+loss: 1*L1
+skip_threshold: 100000000.0
+save: smgarn
+load: 
+resume: 0
+save_models: False
+print_every: 100
+save_results: True
+save_gt: False
+

experiment/smgarn/loss.pt

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+size 980

experiment/smgarn/loss_log.pt

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experiment/smgarn/model/model_best.pt

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experiment/smgarn/model/model_latest.pt

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experiment/smgarn/optimizer.pt

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experiment/smgarn/psnr_log.pt

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