Awesome
IDN-tensorflow
Testing
- Install Tensorflow 1.11, Matlab R2017a
- Download Test datasets
- Modify
config.py
(if you want to test x3 model on Set14,config.TEST.model_path = 'checkpoint_x3/model.ckpt'
config.TEST.dataset = 'Set14'
) andtest.py
(scale = 3
). - Run testing:
python test.py
Training
- Download Training dataset
- Modify
config.py
(if you want to train x4 model,config.TRAIN.hr_img_path = '/path/to/DIV2K_train_HR/'
config.TRAIN.checkpoint_dir = 'checkpoint_x4/'
config.VALID.hr_img_path = '/path/to/DIV2K_valid_HR/'
config.VALID.lr_img_path = '/path/to/DIV2K_valid_LR_x4/'
) andtrain_SR.py
(scale = 4
) - Run training:
python train_SR.py
Note
This TensorFlow version is trained with DIV2K training dataset on RGB channels. Additionally, We modify the upsample layer to subpixel convolution (the original version is transposed convolution).
Results
The following PSNR/SSIMs are evaluated on Matlab R2017a and the code can be referred to Evaluate_PSNR_SSIM.m.
<sub>Training dataset</sub> | <sub>Scale</sub> | <sub>Set5</sub> | <sub>Set14</sub> | <sub>B100</sub> | <sub>Urban100</sub> |
---|---|---|---|---|---|
<sub> 291 </sub> | <sub>×2</sub> | <sub>37.83 / 0.9600<sub> | <sub>33.30 / 0.9148</sub> | <sub>32.08 / 0.8985</sub> | <sub>31.27 / 0.9196</sub> |
<sub> DIV2K </sub> | <sub>×2</sub> | <sub>37.85 / 0.9598<sub> | <sub>33.58 / 0.9178</sub> | <sub>32.11 / 0.8989</sub> | <sub>31.95 / 0.9266</sub> |
<sub> 291 </sub> | <sub>×3</sub> | <sub>34.11 / 0.9253<sub> | <sub>29.99 / 0.8354</sub> | <sub>28.95 / 0.8013</sub> | <sub>27.42 / 0.8359</sub> |
<sub> DIV2K </sub> | <sub>×3</sub> | <sub>34.24 / 0.9260<sub> | <sub>30.27 / 0.8408</sub> | <sub>29.03 / 0.8038</sub> | <sub>27.99 / 0.8489</sub> |
<sub> 291 </sub> | <sub>×4</sub> | <sub>31.82 / 0.8903<sub> | <sub>28.25 / 0.7730</sub> | <sub>27.41 / 0.7297</sub> | <sub>25.41 / 0.7632</sub> |
<sub> DIV2K </sub> | <sub>×4</sub> | <sub>31.99 / 0.8928<sub> | <sub>28.52 / 0.7794</sub> | <sub>27.52 / 0.7339</sub> | <sub>25.92 / 0.7801</sub> |
Model Parameters
<sub>Scale</sub> | <sub>Model size</sub> |
---|---|
<sub>×2</sub> | <sub>579,276</sub> |
<sub>×3</sub> | <sub>587,931</sub> |
<sub>×4</sub> | <sub>600,048</sub> |
Citation
If you find IDN useful in your research, please consider citing:
@inproceedings{Hui-IDN-2018,
title={Fast and Accurate Single Image Super-Resolution via Information Distillation Network},
author={Hui, Zheng and Wang, Xiumei and Gao, Xinbo},
booktitle={CVPR},
pages = {723--731},
year={2018}
}