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# ESRGAN (Enhanced SRGAN) [[Paper]](https://arxiv.org/abs/1809.00219) [[BasicSR]](https://github.com/xinntao/BasicSR)
## :smiley: Training codes are in [BasicSR](https://github.com/xinntao/BasicSR) repo.
### :smiley: Training codes are in [BasicSR](https://github.com/xinntao/BasicSR) repo.
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).
### 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/)
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```
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.
4. Run test. We provide ESRGAN model and RRDB_PSNR model and you can config in the `test.py`.
```
python test.py models/RRDB_ESRGAN_x4.pth
python test.py models/RRDB_PSNR_x4.pth
python test.py
```
5. The results are in `./results` folder.
### Network interpolation demo