Open Source Image and Video Super-Resolution Toolbox
Project description
:rocket: BasicSR
English | 简体中文 GitHub | Gitee码云
:rocket: We add BasicSR-Examples, which provides guidance and templates of using BasicSR as a python package. :rocket:
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
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.
:triangular_flag_on_post: New Features/Updates
- :white_check_mark: Sep 2, 2021. Add SwinIR training and testing codes: SwinIR by Jingyun Liang:+1:. 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.
- :white_check_mark: July 20, 2021. Add dual-blind face restoration codes: HiFaceGAN codes by Lotayou.
- :white_check_mark: Nov 29, 2020. Add ESRGAN and DFDNet colab demo
- :white_check_mark: Sep 8, 2020. Add blind face restoration inference codes: DFDNet.
- :white_check_mark: Aug 27, 2020. Add StyleGAN2 training and testing codes: StyleGAN2.
More
- 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.
: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:
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)
: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 = {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.
:e-mail: Contact
If you have any questions, please email xintao.wang@outlook.com
.
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