🚀 BasicSR

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Google Colab: GitHub Link | Google Drive Link
Ⓜ️ Model Zoo ⏬ Google Drive: Pretrained Models | Reproduced Experiments ⏬ 百度网盘: 预训练模型 | 复现实验
📁 Datasets ⏬ Google Drive ⏬ 百度网盘 (提取码:basr)
📈 Training curves in wandb
💻 Commands for training and testing
⚡ HOWTOs

BasicSR (Basic Super Restoration) is an open source image and video restoration toolbox based on PyTorch, such as super-resolution, denoise, deblurring, JPEG artifacts removal, etc.
_{(ESRGAN, EDVR, DNI, SFTGAN)} _{(HandyView, HandyFigure, HandyCrawler, HandyWriting)}

✨ New Features

Aug 5, 2021. NIQE produces the same results as MATLAB (both are 5.7296 for tests/data/baboon.png).
July 31, 2021. Add bi-directional video super-resolution codes: BasicVSR and IconVSR.
July 20, 2021. Add dual-blind face restoration codes: HiFaceGAN codes by Lotayou.
Nov 29, 2020. Add ESRGAN and DFDNet colab demo
Sep 8, 2020. Add blind face restoration inference codes: DFDNet.
Aug 27, 2020. Add StyleGAN2 training and testing codes: StyleGAN2.

Sep 8, 2020. Add blind face restoration inference codes: DFDNet.
ECCV20: Blind Face Restoration via Deep Multi-scale Component Dictionaries
Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, Wangmeng Zuo and Lei Zhang
Aug 27, 2020. Add StyleGAN2 training and testing codes.
CVPR20: Analyzing and Improving the Image Quality of StyleGAN
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen and Timo Aila
Aug 19, 2020. A brand-new BasicSR v1.0.0 online.

⚡ HOWTOs

We provides simple pipelines to train/test/inference models for quick start. These pipelines/commands cannot cover all the cases and more details are in the following sections.

GAN
StyleGAN2	Train	Inference
Face Restoration
DFDNet	-	Inference
Super Resolution
ESRGAN	TODO	TODO	SRGAN	TODO	TODO
EDSR	TODO	TODO	SRResNet	TODO	TODO
RCAN	TODO	TODO
EDVR	TODO	TODO	DUF	-	TODO
BasicVSR	TODO	TODO	TOF	-	TODO
Deblurring
DeblurGANv2	-	TODO
Denoise
RIDNet	-	TODO	CBDNet	-	TODO

🔧 Dependencies and Installation

For detailed instructions refer to INSTALL.md.

⏳ TODO List

Please see project boards.

🐢 Dataset Preparation

Please refer to DatasetPreparation.md for more details.
The descriptions of currently supported datasets (torch.utils.data.Dataset classes) are in Datasets.md.

💻 Train and Test

Training and testing commands: Please see TrainTest.md for the basic usage.
Options/Configs: Please refer to Config.md.
Logging: Please refer to Logging.md.

🏰 Model Zoo and Baselines

The descriptions of currently supported models are in Models.md.
Pre-trained models and log examples are available in ModelZoo.md.
We also provide training curves in wandb:

📝 Codebase Designs and Conventions

Please see DesignConvention.md for the designs and conventions of the BasicSR codebase.
The figure below shows the overall framework. More descriptions for each component:
Datasets.md | Models.md | Config.md | Logging.md

📜 License and Acknowledgement

This project is released under the Apache 2.0 license.
More details about license and acknowledgement are in LICENSE.

🌏 Citations

If BasicSR helps your research or work, please consider citing BasicSR.
The following is a BibTeX reference. The BibTeX entry requires the url LaTeX package.

@misc{wang2020basicsr,
  author =       {Xintao Wang and Ke Yu and Kelvin C.K. Chan and
                  Chao Dong and Chen Change Loy},
  title =        {{BasicSR}: Open Source Image and Video Restoration Toolbox},
  howpublished = {\url{https://github.com/xinntao/BasicSR}},
  year =         {2020}
}

Xintao Wang, Ke Yu, Kelvin C.K. Chan, Chao Dong and Chen Change Loy. BasicSR: Open Source Image and Video Restoration Toolbox. https://github.com/xinntao/BasicSR, 2020.

📧 Contact

If you have any question, please email xintao.wang@outlook.com.

rsingh2083/BasicSR