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## ESRGAN (Enhanced SRGAN) [[BasicSR]](https://github.com/xinntao/BasicSR) [[EDVR]](https://github.com/xinntao/EDVR) [[DNI]](https://xinntao.github.io/projects/DNI)
## ESRGAN (Enhanced SRGAN) [:rocket: [BasicSR/EDVR](https://github.com/xinntao/BasicSR)]
## We have merged the training codes of ESRGAN into [MMSR](https://github.com/open-mmlab/mmsr) :smile:
MMSR is an open source image and video super-resolution toolbox based on PyTorch. It is a part of the [open-mmlab](https://github.com/open-mmlab) project developed by [Multimedia Laboratory, CUHK](http://mmlab.ie.cuhk.edu.hk/). MMSR is based on our previous projects: [BasicSR](https://github.com/xinntao/BasicSR), [ESRGAN](https://github.com/xinntao/ESRGAN), and [EDVR](https://github.com/xinntao/EDVR).
We have simplified the network structure file.<br/>
You can convert the previously save models (`*.pth`) with the script `transer_RRDB_models.py`;<br/>
If you want to use the old arch, you can find it [here](https://github.com/xinntao/ESRGAN/releases/tag/old-arch).
---
Check out our new work on:<br/>
1. **Video Super-Resolution**: [`EDVR: Video Restoration with Enhanced Deformable Convolutional Networks`](https://xinntao.github.io/projects/EDVR), which has won all four tracks in NTIRE 2019 Challenges on Video Restoration and Enhancement (CVPR19 Workshops).
2. **DNI (CVPR19)**: [`Deep Network Interpolation for Continuous Imagery Effect Transition`](https://xinntao.github.io/projects/DNI)
---
#### The training codes are in :rocket: [BasicSR](https://github.com/xinntao/BasicSR). This repo only provides simple testing codes, pretrained models and the network interpolation demo.
BasicSR is an **open source** image and video super-resolution toolbox based on PyTorch (will extend to more restoration tasks in the future). <br>
It includes methods such as EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, etc. It now also supports StyleGAN2.
### Enhanced Super-Resolution Generative Adversarial Networks
By Xintao Wang, [Ke Yu](https://yuke93.github.io/), Shixiang Wu, [Jinjin Gu](http://www.jasongt.com/), Yihao Liu, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ&hl=en), [Yu Qiao](http://mmlab.siat.ac.cn/yuqiao/), [Chen Change Loy](http://personal.ie.cuhk.edu.hk/~ccloy/)
This repo only provides simple testing codes, pretrained models and the network strategy demo. For full training and testing codes, please refer to [BasicSR](https://github.com/xinntao/BasicSR).
We won the first place in [PIRM2018-SR competition](https://www.pirm2018.org/PIRM-SR.html) (region 3) and got the best perceptual index.
The paper is accepted to [ECCV2018 PIRM Workshop](https://pirm2018.org/).
@ -31,14 +18,7 @@ The paper is accepted to [ECCV2018 PIRM Workshop](https://pirm2018.org/).
> 2. How do you get the perceptual index in your ESRGAN paper?
#### 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}
}
-->
@InProceedings{wang2018esrgan,
author = {Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Loy, Chen Change},
title = {ESRGAN: Enhanced super-resolution generative adversarial networks},