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Rocksugar a7d8512bee | 1 year ago | |
---|---|---|
1_Train_deep_model | 1 year ago | |
2_Transfer_to_LUT | 1 year ago | |
3_Test_using_LUT | 1 year ago | |
.gitignore | 1 year ago | |
Demo.jpg | 1 year ago | |
README.md | 1 year ago |
cd ./1_Train_deep_model
./train
../train/DIV2K_train_HR/*.png
../train/DIV2K_train_LR_bicubic/X4/*.png
.Set5 HR/LR validation png images are already included in ./val
, or you can use other images.
You may modify user parameters in L22 in ./Train_Model_S.py
.
Run.
python Train_Model_S.py
./checkpoint/S
../log/S
.cd ./2_Transfer_to_LUT
./Transfer_Model_S.py
../Model_S.pth
.python Transfer_Model_S.py
./Model_S_x4_4bit_int8.npy
.cd ./3_Test_using_LUT
./Test_Model_S.py
.Set5 HR/LR test images are already included in ./test
, or you can use other images.
Run.
python Test_Model_S.py # Ours-S
python Test_Model_F.py # Ours-F
python Test_Model_V.py # Ours-V
Resulting images will be saved in ./output_S_x4_4bit/*.png
.
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.
Download SR-LUT.apk and install it.
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}
}
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