mirror of
https://github.com/xinntao/ESRGAN.git
synced 2024-08-17 08:30:32 +03:00
update readme
This commit is contained in:
Xintao
parent
435851ee28
commit
12a0ead623
42
README.md
42
README.md
@ -1,26 +1,13 @@
|
||||
## 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},
|
||||
@ -52,7 +32,7 @@ The paper is accepted to [ECCV2018 PIRM Workshop](https://pirm2018.org/).
|
||||
</p>
|
||||
|
||||
The **RRDB_PSNR** PSNR_oriented model trained with DF2K dataset (a merged dataset with [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/) and [Flickr2K](http://cv.snu.ac.kr/research/EDSR/Flickr2K.tar) (proposed in [EDSR](https://github.com/LimBee/NTIRE2017))) is also able to achive high PSNR performance.
|
||||
|
||||
|
||||
| <sub>Method</sub> | <sub>Training dataset</sub> | <sub>Set5</sub> | <sub>Set14</sub> | <sub>BSD100</sub> | <sub>Urban100</sub> | <sub>Manga109</sub> |
|
||||
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
||||
| <sub>[SRCNN](http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html)</sub>| <sub>291</sub>| <sub>30.48/0.8628</sub> |<sub>27.50/0.7513</sub>|<sub>26.90/0.7101</sub>|<sub>24.52/0.7221</sub>|<sub>27.58/0.8555</sub>|
|
||||
@ -67,12 +47,12 @@ The **RRDB_PSNR** PSNR_oriented model trained with DF2K dataset (a merged datase
|
||||
- Python packages: `pip install numpy opencv-python`
|
||||
|
||||
### Test models
|
||||
1. Clone this github repo.
|
||||
1. Clone this github repo.
|
||||
```
|
||||
git clone https://github.com/xinntao/ESRGAN
|
||||
cd ESRGAN
|
||||
```
|
||||
2. Place your own **low-resolution images** in `./LR` folder. (There are two sample images - baboon and comic).
|
||||
2. Place your own **low-resolution images** in `./LR` folder. (There are two sample images - baboon and comic).
|
||||
3. Download pretrained models from [Google Drive](https://drive.google.com/drive/u/0/folders/17VYV_SoZZesU6mbxz2dMAIccSSlqLecY) or [Baidu Drive](https://pan.baidu.com/s/1-Lh6ma-wXzfH8NqeBtPaFQ). Place the models in `./models`. We provide two models with high perceptual quality and high PSNR performance (see [model list](https://github.com/xinntao/ESRGAN/tree/master/models)).
|
||||
4. Run test. We provide ESRGAN model and RRDB_PSNR model and you can config in the `test.py`.
|
||||
```
|
||||
@ -127,7 +107,7 @@ We propose the **network interpolation strategy** to balance the visual quality
|
||||
<img height="500" src="figures/net_interp.jpg">
|
||||
</p>
|
||||
|
||||
We show the smooth animation with the interpolation parameters changing from 0 to 1.
|
||||
We show the smooth animation with the interpolation parameters changing from 0 to 1.
|
||||
Interestingly, it is observed that the network interpolation strategy provides a smooth control of the RRDB_PSNR model and the fine-tuned ESRGAN model.
|
||||
|
||||
<p align="center">
|
||||
@ -135,7 +115,7 @@ Interestingly, it is observed that the network interpolation strategy provides a
|
||||
   
|
||||
<img height="480" src="figures/102061.gif">
|
||||
</p>
|
||||
|
||||
|
||||
## Qualitative Results
|
||||
PSNR (evaluated on the Y channel) and the perceptual index used in the PIRM-SR challenge are also provided for reference.
|
||||
|
||||
@ -163,7 +143,7 @@ The red sign indicates the main improvement compared with the previous model.
|
||||
## BN artifacts
|
||||
We empirically observe that BN layers tend to bring artifacts. These artifacts,
|
||||
namely BN artifacts, occasionally appear among iterations and different settings,
|
||||
violating the needs for a stable performance over training. We find that
|
||||
violating the needs for a stable performance over training. We find that
|
||||
the network depth, BN position, training dataset and training loss
|
||||
have impact on the occurrence of BN artifacts.
|
||||
<p align="center">
|
||||
|
||||
Loading…
Reference in New Issue
Block a user