liuguoyou / sr-lut Goto Github PK

[Paper]

• Python 3.6
• PyTorch
• glob
• numpy
• pillow
• tqdm
• tensorboardx

1. Move into a directory.

cd ./1_Train_deep_model

2. Prepare DIV2K training images into ./train.

• HR images should be placed as ./train/DIV2K_train_HR/*.png.
• LR images should be placed as ./train/DIV2K_train_LR_bicubic/X4/*.png.

3. Set5 HR/LR validation png images are already included in ./val, or you can use other images.

4. You may modify user parameters in L22 in ./Train_Model_S.py.

5. Run.

python Train_Model_S.py

6. Checkpoints will be saved in ./checkpoint/S.

• Training log will be generated in ./log/S.

1. Move into a directory.

cd ./2_Transfer_to_LUT

2. Modify user parameters in L9 in ./Transfer_Model_S.py.

• Specify a saved checkpoint in the step 1, or you can use attached ./Model_S.pth.

3. Run.

python Transfer_Model_S.py

4. The resulting LUT will be saved like ./Model_S_x4_4bit_int8.npy.

1. Move into a directory.

cd ./3_Test_using_LUT

2. Modify user parameters in L17 in ./Test_Model_S.py.

• Specify the generated LUT in the step 2, or use attached LUTs (npy files).

3. Set5 HR/LR test images are already included in ./test, or you can use other images.

4. Run.

python Test_Model_S.py      # Ours-S
python Test_Model_F.py      # Ours-F
python Test_Model_V.py      # Ours-V

5. Resulting images will be saved in ./output_S_x4_4bit/*.png.

6. We can reproduce the results of Table 6 in the paper, by modifying the variable SAMPLING_INTERVAL in L19 in Test_Model_S.py to range 3-8.

1. Download SR-LUT.apk and install it.

2. You can test Set14 images or other images.

@InProceedings{jo2021practical,
   author = {Jo, Younghyun and Kim, Seon Joo},
   title = {Practical Single-Image Super-Resolution Using Look-Up Table},
   booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
   month = {June},
   year = {2021}
}