ESRGAN

## ESRGAN (Enhanced SRGAN) [:rocket: [BasicSR](https://github.com/xinntao/BasicSR)] [[Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN)] :sparkles: **New Updates.** We have extended ESRGAN to [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN), which is a **more practical algorithm for real-world image restoration**. For example, it can also remove annoying JPEG compression artifacts.
You are recommended to have a try :smiley: In the [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) repo, - You can still use the original ESRGAN model or your re-trained ESRGAN model. [The model zoo in Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN#european_castle-model-zoo). - We provide a more handy inference script, which supports 1) **tile** inference; 2) images with **alpha channel**; 3) **gray** images; 4) **16-bit** images. - We also provide a **Windows executable file** `RealESRGAN-ncnn-vulkan` for easier use without installing the environment. This executable file also includes the original ESRGAN model. - The full training codes are also released in the [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) repo. Welcome to open issues or open discussions in the [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) repo. - If you have any question, you can open an issue in the [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) repo. - If you have any good ideas or demands, please open an issue/discussion in the [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) repo to let me know. - If you have some images that Real-ESRGAN could not well restored, please also open an issue/discussion in the [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) repo. I will record it (but I cannot guarantee to resolve it). Here are some examples for Real-ESRGAN:

:book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data > [[Paper](https://arxiv.org/abs/2107.10833)]
> [Xintao Wang](https://xinntao.github.io/), Liangbin Xie, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en)
> Applied Research Center (ARC), Tencent PCG
> Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences ----- As there may be some repos have dependency on this ESRGAN repo, we will not modify this ESRGAN repo (especially the codes). The following is the original README: #### 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](https://github.com/xinntao/BasicSR) is an **open source** image and video super-resolution toolbox based on PyTorch (will extend to more restoration tasks in the future).
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/) 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/). :triangular_flag_on_post: Add [Frequently Asked Questions](https://github.com/xinntao/ESRGAN/blob/master/QA.md). > For instance, > 1. How to reproduce your results in the PIRM18-SR Challenge (with low perceptual index)? > 2. How do you get the perceptual index in your ESRGAN paper? #### BibTeX @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}, booktitle = {The European Conference on Computer Vision Workshops (ECCVW)}, month = {September}, year = {2018} }

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. | Method | Training dataset | Set5 | Set14 | BSD100 | Urban100 | Manga109 | |:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [SRCNN](http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html)| 291| 30.48/0.8628 |27.50/0.7513|26.90/0.7101|24.52/0.7221|27.58/0.8555| | [EDSR](https://github.com/thstkdgus35/EDSR-PyTorch) | DIV2K | 32.46/0.8968 | 28.80/0.7876 | 27.71/0.7420 | 26.64/0.8033 | 31.02/0.9148 | | [RCAN](https://github.com/yulunzhang/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 >= 1.0](https://pytorch.org/) (CUDA version >= 7.5 if installing with CUDA. [More details](https://pytorch.org/get-started/previous-versions/)) - Python packages: `pip install numpy opencv-python` ### Test models 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). 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`. ``` python test.py ``` 5. The results are in `./results` folder. ### Network interpolation demo You can interpolate the RRDB_ESRGAN and RRDB_PSNR models with alpha in [0, 1]. 1. Run `python net_interp.py 0.8`, where *0.8* is the interpolation parameter and you can change it to any value in [0,1]. 2. Run `python test.py models/interp_08.pth`, where *models/interp_08.pth* is the model path.

## Perceptual-driven SR Results You can download all the resutls from [Google Drive](https://drive.google.com/drive/folders/1iaM-c6EgT1FNoJAOKmDrK7YhEhtlKcLx?usp=sharing). (:heavy_check_mark: included; :heavy_minus_sign: not included; :o: TODO) HR images can be downloaed from [BasicSR-Datasets](https://github.com/xinntao/BasicSR#datasets). | Datasets |LR | [*ESRGAN*](https://arxiv.org/abs/1809.00219) | [SRGAN](https://arxiv.org/abs/1609.04802) | [EnhanceNet](http://openaccess.thecvf.com/content_ICCV_2017/papers/Sajjadi_EnhanceNet_Single_Image_ICCV_2017_paper.pdf) | [CX](https://arxiv.org/abs/1803.04626) | |:---:|:---:|:---:|:---:|:---:|:---:| | Set5 |:heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark:| :o: | | Set14 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark:| :o: | | BSDS100 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark:| :o: | | [PIRM](https://pirm.github.io/)
(val, test) | :heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: | :heavy_check_mark:| :heavy_check_mark: | | [OST300](https://arxiv.org/pdf/1804.02815.pdf) |:heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: | :heavy_check_mark:| :o: | | urban100 | :heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: | :heavy_check_mark:| :o: | | [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/)
(val, test) | :heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: | :heavy_check_mark:| :o: | ## ESRGAN We improve the [SRGAN](https://arxiv.org/abs/1609.04802) from three aspects: 1. adopt a deeper model using Residual-in-Residual Dense Block (RRDB) without batch normalization layers. 2. employ [Relativistic average GAN](https://ajolicoeur.wordpress.com/relativisticgan/) instead of the vanilla GAN. 3. 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. Interestingly, it is observed that the network interpolation strategy provides a smooth control of the RRDB_PSNR model and the fine-tuned ESRGAN model.

   

## Qualitative Results PSNR (evaluated on the Y channel) and the perceptual index used in the PIRM-SR challenge are also provided for reference.

## Ablation Study Overall visual comparisons for showing the effects of each component in ESRGAN. Each column represents a model with its configurations in the top. 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 the network depth, BN position, training dataset and training loss have impact on the occurrence of BN artifacts.

## Useful techniques to train a very deep network We find that residual scaling and smaller initialization can help to train a very deep network. More details are in the Supplementary File attached in our [paper](https://arxiv.org/abs/1809.00219).

## The influence of training patch size We observe that training a deeper network benefits from a larger patch size. Moreover, the deeper model achieves more improvement (∼0.12dB) than the shallower one (∼0.04dB) since larger model capacity is capable of taking full advantage of larger training patch size. (Evaluated on Set5 dataset with RGB channels.)