• :rocket: BasicSR
• • :zap: HOWTOs
  • :wrench: Dependencies and Installation
  • :hourglass_flowing_sand: TODO List
  • :turtle: Dataset Preparation
  • :computer: Train and Test
  • :european_castle: Model Zoo and Baselines
  • :memo: Codebase Designs and Conventions
  • :scroll: License and Acknowledgement
  • :earth_asia: Citations
  • :e-mail: Contact

🚀 BasicSR

English | 简体中文   GitHub | Gitee码云

🚀 We add BasicSR-Examples, which provides guidance and templates of using BasicSR as a python package. 🚀

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🧭 入群二维码 (QQ、微信)    入群指南 (腾讯文档)

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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

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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.

BasicSR (Basic Super Restoration) 是一个基于 PyTorch 的开源 图像视频复原工具箱, 比如 超分辨率, 去噪, 去模糊, 去 JPEG 压缩噪声等.

🚩 New Features/Updates

• ✅ Oct 5, 2021. Add ECBSR training and testing codes: ECBSR.

  ACMMM21: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices

• ✅ Sep 2, 2021. Add SwinIR training and testing codes: SwinIR by Jingyun Liang. More details are in HOWTOs.md
• ✅ Aug 5, 2021. Add NIQE, which 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.

  CVPR21: BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

• More

✨ Projects that use BasicSR

• Real-ESRGAN: A practical algorithm for general image restoration
• GFPGAN: A practical algorithm for real-world face restoration

If you use BasicSR in your open-source projects, welcome to contact me (by email or opening an issue/pull request). I will add your projects to the above list 😊

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If BasicSR helps your research or work, please help to ⭐ this repo or recommend it to your friends. Thanks😊

Other recommended projects:

▶️ Real-ESRGAN: A practical algorithm for general image restoration

▶️ GFPGAN: A practical algorithm for real-world face restoration

▶️ facexlib: A collection that provides useful face-relation functions.

▶️ HandyView: A PyQt5-based image viewer that is handy for view and comparison.

_{(ESRGAN, EDVR, DNI, SFTGAN)
(HandyView, HandyFigure, HandyCrawler, HandyWriting)}

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⚡ HOWTOs

We provide simple pipelines to train/test/inference models for a 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	SwinIR	Train	Inference
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 questions, please email xintao.wang@outlook.com.