##
[](https://github.com/xinntao/BasicSR/blob/master/LICENSE/LICENSE)
[](https://pypi.org/project/basicsr/)
[](https://lgtm.com/projects/g/xinntao/BasicSR/context:python)
[](https://github.com/xinntao/BasicSR/blob/master/.github/workflows/pylint.yml)
[](https://github.com/xinntao/BasicSR/blob/master/.github/workflows/publish-pip.yml)
[](https://github.com/xinntao/BasicSR/blob/master/.github/workflows/gitee-mirror.yml)
:rocket: We add [BasicSR-Examples](https://github.com/xinntao/BasicSR-examples), which provides guidance and templates of using BasicSR as a python package. :rocket:
:loudspeaker: **技术交流QQ群**：**320960100** 入群答案：**互帮互助共同进步**
:compass: [入群二维码](#e-mail-contact) (QQ、微信) [入群指南 (腾讯文档)](https://docs.qq.com/doc/DYXBSUmxOT0xBZ05u)
---
Google Colab: [GitHub Link](colab) **|** [Google Drive Link](https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing)
:m: [Model Zoo](docs/ModelZoo.md): :arrow_double_down: Google Drive: [Pretrained Models](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing) **|** [Reproduced Experiments](https://drive.google.com/drive/folders/1XN4WXKJ53KQ0Cu0Yv-uCt8DZWq6uufaP?usp=sharing)
:arrow_double_down: 百度网盘: [预训练模型](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g) **|** [复现实验](https://pan.baidu.com/s/1UElD6q8sVAgn_cxeBDOlvQ)
:file_folder: [Datasets](docs/DatasetPreparation.md): :arrow_double_down: [Google Drive](https://drive.google.com/drive/folders/1gt5eT293esqY0yr1Anbm36EdnxWW_5oH?usp=sharing) :arrow_double_down: [百度网盘](https://pan.baidu.com/s/1AZDcEAFwwc1OC3KCd7EDnQ) (提取码:basr)
:chart_with_upwards_trend: [Training curves in wandb](https://app.wandb.ai/xintao/basicsr)
:computer: [Commands for training and testing](docs/TrainTest.md)
:zap: [HOWTOs](#zap-howtos)
---
BasicSR (**Basic** **S**uper **R**estoration) is an open-source **image and video restoration** toolbox based on PyTorch, such as super-resolution, denoise, deblurring, JPEG artifacts removal, *etc*.
BasicSR (**Basic** **S**uper **R**estoration) 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](https://github.com/xindongzhang/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](https://github.com/JingyunLiang/SwinIR) by [Jingyun Liang](https://github.com/JingyunLiang). More details are in [HOWTOs.md](docs/HOWTOs.md#how-to-train-swinir-sr)
- :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](https://arxiv.org/abs/2012.02181).
> CVPR21: BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
- **[More](docs/history_updates.md)**
:sparkles: **Projects that use BasicSR**
- [**Real-ESRGAN**](https://github.com/xinntao/Real-ESRGAN): A practical algorithm for general image restoration
- [**GFPGAN**](https://github.com/TencentARC/GFPGAN): A practical algorithm for real-world face restoration
If you use `BasicSR` in your open-source projects, welcome to contact me (by [email](#e-mail-contact) 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](https://github.com/xinntao/Real-ESRGAN): A practical algorithm for general image restoration
:arrow_forward: [GFPGAN](https://github.com/TencentARC/GFPGAN): A practical algorithm for real-world face restoration
:arrow_forward: [facexlib](https://github.com/xinntao/facexlib): A collection that provides useful face-relation functions.
:arrow_forward: [HandyView](https://github.com/xinntao/HandyView): A PyQt5-based image viewer that is handy for view and comparison.
([ESRGAN](https://github.com/xinntao/ESRGAN), [EDVR](https://github.com/xinntao/EDVR), [DNI](https://github.com/xinntao/DNI), [SFTGAN](https://github.com/xinntao/SFTGAN))
([HandyView](https://github.com/xinntao/HandyView), [HandyFigure](https://github.com/xinntao/HandyFigure), [HandyCrawler](https://github.com/xinntao/HandyCrawler), [HandyWriting](https://github.com/xinntao/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](docs/HOWTOs.md#How-to-train-StyleGAN2) | [Inference](docs/HOWTOs.md#How-to-inference-StyleGAN2) | | | |
| **Face Restoration** | | | | | |
| DFDNet | - | [Inference](docs/HOWTOs.md#How-to-inference-DFDNet) | | | |
| **Super Resolution** | | | | | |
| ESRGAN | *TODO* | *TODO* | SRGAN | *TODO* | *TODO* |
| EDSR | *TODO* | *TODO* | SRResNet | *TODO* | *TODO* |
| RCAN | *TODO* | *TODO* | SwinIR | [Train](docs/HOWTOs.md#how-to-train-swinir-sr) | [Inference](docs/HOWTOs.md#how-to-inference-swinir-sr) |
| 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](INSTALL.md).
## :hourglass_flowing_sand: TODO List
Please see [project boards](https://github.com/xinntao/BasicSR/projects).
## :turtle: Dataset Preparation
- Please refer to **[DatasetPreparation.md](docs/DatasetPreparation.md)** for more details.
- The descriptions of currently supported datasets (`torch.utils.data.Dataset` classes) are in [Datasets.md](docs/Datasets.md).
## :computer: Train and Test
- **Training and testing commands**: Please see **[TrainTest.md](docs/TrainTest.md)** for the basic usage.
- **Options/Configs**: Please refer to [Config.md](docs/Config.md).
- **Logging**: Please refer to [Logging.md](docs/Logging.md).
## :european_castle: Model Zoo and Baselines
- The descriptions of currently supported models are in [Models.md](docs/Models.md).
- **Pre-trained models and log examples** are available in **[ModelZoo.md](docs/ModelZoo.md)**.
- We also provide **training curves** in [wandb](https://app.wandb.ai/xintao/basicsr):
## :memo: Codebase Designs and Conventions
Please see [DesignConvention.md](docs/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](docs/Datasets.md)** | **[Models.md](docs/Models.md)** | **[Config.md](docs/Config.md)** | **[Logging.md](docs/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](LICENSE/README.md).
## :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.
``` latex
@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. , 2018.
## :e-mail: Contact
If you have any questions, please email `xintao.wang@outlook.com`.
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