Awesome

RDN

This repository is implementation of the "Residual Dense Network for Image Super-Resolution".

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
DIV2K	3	Train	Download
DIV2K	4	Train	Download
Set5	2	Eval	Download
Set5	3	Eval	Download
Set5	4	Eval	Download

Otherwise, you can use prepare.py to create custom dataset.

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
RDN (D=16, C=8, G=64, G0=64)	3	Download
RDN (D=16, C=8, G=64, G0=64)	4	Download

The results are stored in the same path as the query image.

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}
}