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IDN-tensorflow

[Original Caffe version]

Testing

python test.py

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

Test_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}
}