{"id":25999723,"url":"https://github.com/XLearning-SCU/Awesome-All-In-One-Image-Restoration","last_synced_at":"2025-03-05T18:41:21.188Z","repository":{"id":235807871,"uuid":"791293644","full_name":"XLearning-SCU/Awesome-All-In-One-Image-Restoration","owner":"XLearning-SCU","description":"This is a summary of research on All-In-One Image/Video Restoration. There may be omissions. If anything is missing please get in touch with us. Our emails: liboyun.gm@gmail.com; gouyuanbiao@gmail.com; haiyuzhao.gm@gmail.com; wangwenxin.gm@gmail.com","archived":false,"fork":false,"pushed_at":"2025-01-14T12:53:09.000Z","size":29,"stargazers_count":151,"open_issues_count":1,"forks_count":5,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-14T13:54:58.142Z","etag":null,"topics":["all-in-one-image-restoration","all-in-one-video-restoration","image-restoration","video-restoration"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/XLearning-SCU.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-04-24T13:05:28.000Z","updated_at":"2025-01-14T12:53:12.000Z","dependencies_parsed_at":"2024-04-24T16:41:03.088Z","dependency_job_id":"e03f6d26-23b4-48ec-9541-aa321209d908","html_url":"https://github.com/XLearning-SCU/Awesome-All-In-One-Image-Restoration","commit_stats":null,"previous_names":["xlearning-scu/awesome-all-in-one-image-restoration"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/XLearning-SCU%2FAwesome-All-In-One-Image-Restoration","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/XLearning-SCU%2FAwesome-All-In-One-Image-Restoration/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/XLearning-SCU%2FAwesome-All-In-One-Image-Restoration/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/XLearning-SCU%2FAwesome-All-In-One-Image-Restoration/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/XLearning-SCU","download_url":"https://codeload.github.com/XLearning-SCU/Awesome-All-In-One-Image-Restoration/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242082881,"owners_count":20069203,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["all-in-one-image-restoration","all-in-one-video-restoration","image-restoration","video-restoration"],"created_at":"2025-03-05T18:40:45.445Z","updated_at":"2025-03-05T18:41:21.182Z","avatar_url":"https://github.com/XLearning-SCU.png","language":null,"funding_links":[],"categories":["Other Lists","Others"],"sub_categories":["TeX Lists"],"readme":"# Awesome-All-In-One-Image-Restoration (Updating)\n\nThis curated list of papers related to all-in-one image/video restoration. All-in-one image/video restoration aims to handle multiple degradations with one model [[1]](#AirNet). \n\nWe mark works contributed by ourselves with ⭐.\n\n*This repository now is maintained by [Boyun Li](https://liboyun.github.io/), [Yuanbiao Gou](https://ybgou.github.io/) and [Haiyu Zhao](https://pandint.github.io/about/), feel free to contact us if you have any questions.*\n\n# TODO\n\u003c!-- Blind All-in-one Restoration (BAR) is an emerging NEW research DIRECTION of image and video restoration. BAR aims to address multiple unknown types of degradations in a unified framework, rather than handling each known degradation separately as in traditional approaches.  --\u003e\n\n\u003c!-- XLearning group devotes to pushing image and video restoration towards more general application scenarios. Specifically, we have introduced several pioneering solutions: one of the first blind all-in-one image restoration network (AirNet, CVPR 2022), the first open-set image restoration method (TAO, ICML 2024), and the first blind all-in-one video restoration for time-varying degradations (AverNet, NeurIPS 2024). --\u003e\n\n## Table of Contents\n\n- [All-In-One Image Restoration](#All-in-one-image-restoration)\n  - [Open-set Image Restoration](#Open-set-Image-Restoration)\n  - [All-In-One Image Restoration](#All-In-One-Image-Restoration)\n- [All-In-One Video Restoration](#All-In-One-Video-Restoration)\n- [Misc](#Misc)\n\n## All-In-One Image Restoration\n\n### Open-set Image Restoration\n\n- `[2024 ICML]` ⭐ **Test-Time Degradation Adaption for Open-Set Image Restoration**  \n*Yuanbiao Gou, Haiyu Zhao, Boyun Li, Xinyan Xiao, Xi Peng*  \n[[paper]](https://arxiv.org/abs/2312.02197) [[code]](https://github.com/XLearning-SCU/2024-ICML-TAO)\n\n### All-In-One Image Restoration\n\n#### 2022\n\n- `[2022 CVPR]` ⭐ **\u003ca id=\"AirNet\"\u003eAll-In-One Image Restoration for Unknown Corruption\u003c/a\u003e**  \n*Boyun Li, Xiao Liu, Peng Hu, Zhongqin Wu, Jiancheng Lv, Xi Peng*   \n[[paper]](http://pengxi.me/wp-content/uploads/2022/03/All-In-One-Image-Restoration-for-Unknown-Corruption.pdf) [[code]](https://github.com/XLearning-SCU/2022-CVPR-AirNet.git) [![](https://img.shields.io/github/stars/XLearning-SCU/2022-CVPR-AirNet?style=social\u0026label=Stars)](https://github.com/XLearning-SCU/2022-CVPR-AirNet)\n\n- `[2022 CVPR]` **TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions**  \n*Jeya Maria Jose Valanarasu, Rajeev Yasarla, Vishal M. Patel*   \n[[paper]](https://arxiv.org/abs/2111.14813) [[code]](https://github.com/jeya-maria-jose/TransWeather.git)\n\n#### 2023\n\n- `[2023 NeurIPS]` **PromptIR: Prompting for All-in-One Blind Image Restoration**  \n*Vaishnav Potlapalli, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan*   \n[[paper]](https://arxiv.org/abs/2306.13090) [[code]](https://github.com/va1shn9v/PromptIR.git)\n\n- `[2023 CVPR]` **Ingredient-oriented Multi-Degradation Learning for Image Restoration**  \n*Jinghao Zhang, Jie Huang, Mingde Yao, Zizheng Yang, Hu Yu, Man Zhou, Feng Zhao*  \n[[paper]](https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Ingredient-Oriented_Multi-Degradation_Learning_for_Image_Restoration_CVPR_2023_paper.pdf) [[code]](https://github.com/JingHao99/IDR-Ingredients-oriented-Degradation-Reformulation.git)\n\n- `[2023 ICCV]` **Adverse Weather Removal with Codebook Priors**  \n*Tian Ye, Sixiang Chen, Jinbin Bai, Jun Shi, Chenghao Xue, Jingxia Jiang, Junjie Yin, Erkang Chen, Yun Liu*  \n[[paper]](https://openaccess.thecvf.com/content/ICCV2023/papers/Ye_Adverse_Weather_Removal_with_Codebook_Priors_ICCV_2023_paper.pdf) [[code]](https://github.com/Owen718/AWRCP.git)\n\n- `[2023 TPAMI]` **Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models**  \n*Ozan Özdenizci, Robert Legenstein*   \n[[paper]](https://arxiv.org/abs/2207.14626) [[code]](https://github.com/IGITUGraz/WeatherDiffusion.git)\n\n- `[2023 Arxiv]` **Multimodal Prompt Perceiver: Empower Adaptiveness, Generalizability and Fidelity for All-in-One Image Restoration**  \n*Yuang Ai, Huaibo Huang, Xiaoqiang Zhou, Jiexiang Wang, Ran He*  \n[[paper]](https://arxiv.org/abs/2312.02918)\n\n- `[2023 Arxiv]` **Prompt-In-Prompt Learning for Universal Image Restoration**  \n*Zilong Li, Yiming Lei, Chenglong Ma, Junping Zhang, Hongming Shan*  \n[[paper]](https://arxiv.org/abs/2312.05038v1) [[code]](https://github.com/longzilicart/pip_universal)\n\n- `[2023 Arxiv]` **RDM-IR: Task-Adaptive Deep Unfolding Network for All-In-One Image Restoration**  \n*Yuanshuo Cheng, Mingwen Shao, Yecong Wan, Chao Wang*   \n[[paper]](https://arxiv.org/abs/2307.07688) [[code]](https://github.com/YuanshuoCheng/RDM-IR)\n\n- `[2023 Arxiv]` **Language-driven All-in-one Adverse Weather Removal**  \n*Hao Yang, Liyuan Pan, Yan Yang, Wei Liang*  \n[[paper]](https://arxiv.org/abs/2312.01381)\n\n- `[2023 Arxiv]` **Always Clear Days: Degradation Type and Severity Aware All-In-One Adverse Weather Removal**  \n*Yu-Wei Chen, Soo-Chang Pei*  \n[[paper]](https://arxiv.org/abs/2310.18293) [[code]](https://github.com/fordevoted/UtilityIR)\n\n#### 2024\n\n- `[2024 ICLR]` **Controlling Vision-Language Models for Universal Image Restoration**  \n*Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön*  \n[[paper]](https://arxiv.org/abs/2310.01018) [[code]](https://github.com/Algolzw/daclip-uir.git)\n\n- `[2024 CVPR]` **Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model**  \n*Dian Zheng, Xiao-Ming Wu, Shuzhou Yang, Jian Zhang, Jian-Fang Hu, Wei-Shi Zheng*  \n[[paper]](https://arxiv.org/abs/2403.11157) [[code]](https://github.com/iSEE-Laboratory/DiffUIR?tab=readme-ov-file)\n\n- `[2024 ECCV]` **Restoring Images in Adverse Weather Conditions via Histogram Transformer**  \n*Shangquan Sun, Wenqi Ren, Xinwei Gao, Rui Wang, Xiaochun Cao*  \n[[paper]](https://arxiv.org/abs/2407.10172) [[code]](https://github.com/sunshangquan/Histoformer)\n\n- `[2024 ECCV]` **AutoDIR: Automatic All-in-One Image Restoration with Latent Diffusion**  \n*Yitong Jiang, Zhaoyang Zhang, Tianfan Xue, Jinwei Gu*   \n[[paper]](https://arxiv.org/pdf/2310.10123) [[code]](https://github.com/jiangyitong/AutoDIR.git)\n\n- `[2024 ACM MM]` **Learning A Low-Level Vision Generalist via Visual Task Prompt**  \n*Xiangyu Chen, Yihao Liu, Yuandong Pu, Wenlong Zhang, Jiantao Zhou, Yu Qiao, Chao Dong*  \n[[paper]](http://arxiv.org/abs/2408.08601) [[code]](https://github.com/chxy95/GenLV)\n\n- `[2024 MICCAI]` **All-In-One Medical Image Restoration via Task-Adaptive Routing**  \n*Zhiwen Yang, Haowei Chen, Ziniu Qian, Yang Yi, Hui Zhang, Dan Zhao, Bingzheng Wei, Yan Xu*  \n[[paper]](https://arxiv.org/abs/2405.19769) [[code]](https://github.com/Yaziwel/All-In-One-Medical-Image-Restoration-via-Task-Adaptive-Routing)\n\n- `[2024 Arxiv]` **AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation**  \n*Yuning Cui, Syed Waqas Zamir, Salman Khan, Alois Knoll, Mubarak Shah, Fahad Shahbaz Khan*   \n[[paper]](https://arxiv.org/abs/2403.14614) [[code]](https://github.com/c-yn/AdaIR.git)\n\n### Non-Blind All-In-One Image Restoration\n\n#### 2020\n\n- `[2020 CVPR]` **All in One Bad Weather Removal Using Architectural Search**  \n*Ruoteng Li, Robby T. Tan, Loong-Fah Cheong*   \n[[paper]](https://openaccess.thecvf.com/content_CVPR_2020/html/Li_All_in_One_Bad_Weather_Removal_Using_Architectural_Search_CVPR_2020_paper.html)\n\n#### 2021\n\n- `[2021 CVPR]` **Pre-Trained Image Processing Transformer**   \n*Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, Wen Gao*   \n[[paper]](https://arxiv.org/abs/2012.00364) [[code]](https://github.com/huawei-noah/Pretrained-IPT.git)\n\n- `[2021 TPAMI]` **A General Decoupled Learning Framework for Parameterized Image Operators**  \n*Qingnan Fan, Dongdong Chen, Lu Yuan, Gang Hua, Nenghai Yu, Baoquan Chen*   \n[[paper]](https://arxiv.org/abs/1907.05852) [[code]](https://github.com/fqnchina/DecoupleLearning.git)\n\n#### 2022\n\n- `[2022 ECCV]` **TAPE: Task-Agnostic Prior Embedding for Image Restoration**  \n*Lin Liu, Lingxi Xie, Xiaopeng Zhang, Shanxin Yuan, Xiangyu Chen, Wengang Zhou, Houqiang Li, Qi Tian*  \n[[paper]](https://arxiv.org/abs/2203.06074) [[code]](http://home.ustc.edu.cn/~ll0825/project_TAPE.html)\n\n#### 2023\n\n- `[2023 CVPR]` **Generative Diffusion Prior for Unified Image Restoration and Enhancement**  \n*Ben Fei, Zhaoyang Lyu, Liang Pan, Junzhe Zhang, Weidong Yang, Tianyue Luo, Bo Zhang, Bo Dai*  \n[[paper]](https://arxiv.org/abs/2304.01247) [[code]](https://github.com/Fayeben/GenerativeDiffusionPrior.git)\n\n- `[2023 CVPR]` **Learning Weather-General and Weather-Specific Features for Image Restoration Under Multiple Adverse Weather Conditions**  \n*Yurui Zhu, Tianyu Wang, Xueyang Fu, Xuanyu Yang, Xin Guo, Jifeng Dai, Yu Qiao, Xiaowei Hu*  \n[[paper]](https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_Learning_Weather-General_and_Weather-Specific_Features_for_Image_Restoration_Under_Multiple_CVPR_2023_paper.pdf) [[code]](https://github.com/zhuyr97/WGWS-Net.git)\n\n- `[2023 IJCAI]` **On Efficient Transformer-Based Image Pre-training for Low-Level Vision**  \n*Wenbo Li, Xin Lu, Shengju Qian, Jiangbo Lu, Xiangyu Zhang, Jiaya Jia*  \n[[paper]](https://arxiv.org/abs/2112.10175) [[code]](https://github.com/fenglinglwb/EDT.git)\n\n- `[2023 Arxiv]` **Exploring Degradation-aware Visual Prompt for Universal Image Restoration**  \n  *Jiaqi Ma, Tianheng Cheng, Guoli Wang, Qian Zhang, Xinggang Wang, Lefei Zhang*  \n  [[paper]](https://arxiv.org/abs/2306.13653) [[code]](https://github.com/leonmakise/ProRes.git)\n\n#### 2024\n\n- `[2024 ECCV]` **InstructIR: High-Quality Image Restoration Following Human Instructions**  \n*Marcos V. Conde, Gregor Geigle, Radu Timofte*  \n[[paper]](https://arxiv.org/abs/2401.16468) [[code]](https://github.com/mv-lab/InstructIR.git)\n\n\n## All-In-One Video Restoration\n\n#### 2023\n\n- `[2023 ICCV]` **Video Adverse-Weather-Component Suppression Network via Weather Messenger and Adversarial Backpropagation**  \n*Yijun Yang, Angelica I. Aviles-Rivero, Huazhu Fu, Ye Liu, Weiming Wang, Lei Zhu*  \n[[paper]](https://openaccess.thecvf.com/content/ICCV2023/html/Yang_Video_Adverse-Weather-Component_Suppression_Network_via_Weather_Messenger_and_Adversarial_Backpropagation_ICCV_2023_paper.html) [[code]](https://github.com/scott-yjyang/ViWS-Net.git)\n\n- `[2023 Arxiv]` **Cross-Consistent Deep Unfolding Network for Adaptive All-In-One Video Restoration**  \n*Yuanshuo Cheng, Mingwen Shao, Yecong Wan, Yuanjian Qiao, Wangmeng Zuo, Deyu Meng*   \n[[paper]](https://arxiv.org/abs/2309.01627)\n\n#### 2024\n\n- `[2024 NeurIPS]` ⭐ **AverNet: All-in-one Video Restoration for Time-varying Unknown Degradations**  \n*Haiyu Zhao, Lei Tian, Xinyan Xiao, Peng Hu, Yuanbiao Gou, Xi Peng*   \n[[paper]](https://openreview.net/pdf/cd985f5642f31d02e47d062bc783deb7c2d1fa8a.pdf) [[code]](https://github.com/XLearning-SCU/2024-NeurIPS-AverNet)\n\n- `[2024 CVPR]` **Genuine Knowledge from Practice: Diffusion Test-Time Adaptation for Video Adverse Weather Removal**  \n*Yijun Yang, Hongtao Wu, Angelica I. Aviles-Rivero, Yulun Zhang, Jing Qin, Lei Zhu*   \n[[paper]](https://arxiv.org/abs/2403.07684) [[code]](https://github.com/scott-yjyang/DiffTTA)\n\n## Misc\n\n- `[2022 Arxiv]` **Relationship Quantification of Image Degradations**  \n*Wenxin Wang, Boyun Li, Yuanbiao Gou, Peng Hu, Xi Peng*  \n[[paper]](https://arxiv.org/abs/2212.04148)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FXLearning-SCU%2FAwesome-All-In-One-Image-Restoration","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FXLearning-SCU%2FAwesome-All-In-One-Image-Restoration","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FXLearning-SCU%2FAwesome-All-In-One-Image-Restoration/lists"}