figures | ||
LR | ||
models | ||
results | ||
architecture.py | ||
block.py | ||
net_interp.py | ||
README.md | ||
test.py |
ESRGAN (Enhanced SRGAN) [Paper] [BasicSR]
Enhanced Super-Resolution Generative Adversarial Networks
By Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Yu Qiao, Xiaoou Tang
This repo only provides simple testing codes, pretrained models and the netwrok strategy demo.
😃 For full training and testing codes, please refer to BasicSR.
We won the first place in PIRM2018-SR competition (region 3) and got the best perceptual index. The paper is accepted to ECCV2018 PIRM Workshop.
BibTeX
@article{wang2018esrgan,
author={Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Loy, Chen Change and Qiao, Yu and Tang, Xiaoou},
title={ESRGAN: Enhanced super-resolution generative adversarial networks},
journal={arXiv preprint arXiv:1809.00219},
year={2018}
}
The RRDB_PSNR PSNR_oriented model trained with DF2K dataset (a merged dataset with DIV2K and Flickr2K (proposed in EDSR)) is also able to achive high PSNR performance.
Method | Training dataset | Set5 | Set14 | BSD100 | Urban100 | Manga109 |
---|---|---|---|---|---|---|
SRCNN | 291 | 30.48/0.8628 | 27.50/0.7513 | 26.90/0.7101 | 24.52/0.7221 | 27.58/0.8555 |
EDSR | DIV2K | 32.46/0.8968 | 28.80/0.7876 | 27.71/0.7420 | 26.64/0.8033 | 31.02/0.9148 |
RCAN | DIV2K | 32.63/0.9002 | 28.87/0.7889 | 27.77/0.7436 | 26.82/ 0.8087 | 31.22/ 0.9173 |
RRDB(ours) | DF2K | 32.73/0.9011 | 28.99/0.7917 | 27.85/0.7455 | 27.03/0.8153 | 31.66/0.9196 |
Quick Test
Dependencies
- Python 3
- PyTorch >= 0.4.0
- Python package
cv2
,numpy
Test
- Clone this github repo.
git clone https://github.com/xinntao/ESRGAN
cd ESRGAN
- Place your own low-resolution images in
./LR
folder. (There are two sample images - baboon and comic). - Download pretrained models from Google Drive or Baidu Drive. Place the models in
./models
. We provide two models with high perceptual quality and high PSNR performance (see model list). - Run test. We provide ESRGAN model and RRDB_PSNR model.
python test.py models/RRDB_ESRGAN_x4.pth
python test.py models/RRDB_PSNR_x4.pth
- The results are in
./results
folder.
Introduction
We improve the SRGAN from three aspects:
- adopt a deeper model using Residual-in-Residual Dense Block (RRDB) without batch normalization layers.
- employ Relativistic average GAN instead of vanilla GAN.
- improve the perceptual loss by using the features before activation.
In contrast to SRGAN, which claimed that deeper models are increasingly difficult to train, our deeper ESRGAN model shows its superior performance with easy training.
Network Interpolation
We propose the network interpolation strategy to balance the visual quality and PSNR.
We show the smooth animation with the interpolation parameters changing from 0 to 1.
Qualitative Results
PSNR (evaluated on the luminance channel in YCbCr color space) and the perceptual index used in the PIRM-SR challenge are also provided for reference.