RDN-pytor ch: PyTor ch实施残差密集网络以实现图像超分辨率(CVPR 2018) -源码

# RDN This repository is implementation of the ["Residual Dense Network for Image Super-Resolution"](https://arxiv.org/abs/1802.08797). <center><img src="./thumbnails/fig1.png"></center> <center><img src="./thumbnails/fig2.png"></center> ## Requirements - PyTorch 1.0.0 - Numpy 1.15.4 - Pillow 5.4.1 - h5py 2.8.0 - tqdm 4.30.0 ## Train The DIV2K, Set5 dataset converted to HDF5 can be downloaded from the links below. | Dataset | Scale | Type | Link | |---------|-------|------|------| | DIV2K | 2 | Train | [Download](https://www.dropbox.com/s/41sn4eie37hp6rh/DIV2K_x2.h5?dl=0) | | DIV2K | 3 | Train | [Download](https://www.dropbox.com/s/4piy2lvhrjb2e54/DIV2K_x3.h5?dl=0) | | DIV2K | 4 | Train | [Download](https://www.dropbox.com/s/ie4a6t7f9n5lgco/DIV2K_x4.h5?dl=0) | | Set5 | 2 | Eval | [Download](https://www.dropbox.com/s/b7v5vis8duh9vwd/Set5_x2.h5?dl=0) | | Set5 | 3 | Eval | [Download](https://www.dropbox.com/s/768b07ncpdfmgs6/Set5_x3.h5?dl=0) | | Set5 | 4 | Eval | [Download](https://www.dropbox.com/s/rtu89xyatbb71qv/Set5_x4.h5?dl=0) | Otherwise, you can use `prepare.py` to create custom dataset. ```bash python train.py --train-file "BLAH_BLAH/DIV2K_x4.h5" \ --eval-file "BLAH_BLAH/Set5_x4.h5" \ --outputs-dir "BLAH_BLAH/outputs" \ --scale 4 \ --num-features 64 \ --growth-rate 64 \ --num-blocks 16 \ --num-layers 8 \ --lr 1e-4 \ --batch-size 16 \ --patch-size 32 \ --num-epochs 800 \ --num-workers 8 \ --seed 123 ``` ## Test Pre-trained weights can be downloaded from the links below. | Model | Scale | Link | |-------|-------|------| | RDN (D=16, C=8, G=64, G0=64) | 2 | [Download](https://www.dropbox.com/s/pd52pkmaik1ri0h/rdn_x2.pth?dl=0) | | RDN (D=16, C=8, G=64, G0=64) | 3 | [Download](https://www.dropbox.com/s/56topxdwm6rakwd/rdn_x3.pth?dl=0) | | RDN (D=16, C=8, G=64, G0=64) | 4 | [Download](https://www.dropbox.com/s/yphiyivb1v7jya2/rdn_x4.pth?dl=0) | The results are stored in the same path as the query image. ```bash python test.py --weights-file "BLAH_BLAH/rdn_x4.pth" \ --image-file "data/119082.png" \ --scale 4 \ --num-features 64 \ --growth-rate 64 \ --num-blocks 16 \ --num-layers 8 ``` ## Results PSNR was calculated on the Y channel. ### Set5 | Eval. Mat | Scale | RDN (Paper) | RDN (Ours) | |-----------|-------|-------|-----------------| | PSNR | 2 | 38.24 | 38.18 | | PSNR | 3 | 34.71 | 34.73 | | PSNR | 4 | 32.47 | 32.40 | <table> <tr> <td><center>Original</center></td> <td><center>BICUBIC x4</center></td> <td><center>RDN x4 (25.08 dB)</center></td> </tr> <tr> <td> <center><img src="./data/119082.png""></center> </td> <td> <center><img src="./data/119082_bicubic_x4.png"></center> </td> <td> <center><img src="./data/119082_rdn_x4.png"></center> </td> </tr> <tr> <td><center>Original</center></td> <td><center>BICUBIC x4</center></td> <td><center>RDN x4 (32.98 dB)</center></td> </tr> <tr> <td> <center><img src="./data/img_043.png""></center> </td> <td> <center><img src="./data/img_043_bicubic_x4.png"></center> </td> <td> <center><img src="./data/img_043_rdn_x4.png"></center> </td> </tr> </table> ## Citation ``` @InProceedings{Lim_2017_CVPR_Workshops, author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu}, title = {Enhanced Deep Residual Networks for Single Image Super-Resolution}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {July}, year = {2017} } @inproceedings{zhang2018residual, title={Residual Dense Network for Image Super-Resolution}, author={Zhang, Yulun and Tian, Yapeng and Kong, Yu and Zhong, Bineng and Fu, Yun}, booktitle={CVPR}, year={2018} } @article{zhang2020rdnir, title={Residual Dense Network for Image Restoration}, author={Zhang, Yulun and Tian, Yapeng and Kong, Yu and Zhong, Bineng and Fu, Yun}, journal={TPAMI}, year={2020} } ```