:rocket: BasicSR

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

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Google Colab: GitHub Link | Google Drive Link
:m: Model Zoo: :arrow_double_down: Google Drive: Pretrained Models | Reproduced Experiments :arrow_double_down: 百度网盘: 预训练模型 | 复现实验
:file_folder: Datasets: :arrow_double_down: Google Drive :arrow_double_down: 百度网盘 (提取码:basr)
:chart_with_upwards_trend: Training curves in wandb
:computer: Commands for training and testing
:zap: 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.

:triangular_flag_on_post: New Features/Updates

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

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

• :white_check_mark: Sep 2, 2021. Add SwinIR training and testing codes: SwinIR by Jingyun Liang. More details are in HOWTOs.md
• :white_check_mark: Aug 5, 2021. Add NIQE, which produces the same results as MATLAB (both are 5.7296 for tests/data/baboon.png).
• :white_check_mark: 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

:sparkles: 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 :blush:

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If BasicSR helps your research or work, please help to :star: this repo or recommend it to your friends. Thanks:blush:
Other recommended projects:
:arrow_forward: Real-ESRGAN: A practical algorithm for general image restoration
:arrow_forward: GFPGAN: A practical algorithm for real-world face restoration
:arrow_forward: facexlib: A collection that provides useful face-relation functions.
:arrow_forward: 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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:zap: 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

:wrench: Dependencies and Installation

For detailed instructions refer to INSTALL.md.

:hourglass_flowing_sand: TODO List

Please see project boards.

:turtle: 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.

:computer: 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.

:european_castle: 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:

:memo: 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

:scroll: License and Acknowledgement

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

:earth_asia: 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 =         {2018}
}

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, 2018.

:e-mail: Contact

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

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