{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/all-in-one-image-restoration-for-unknown","title":"All-in-One Image Restoration for Unknown Corruption","arxiv_id":null,"date":"2022-01-01","proceeding":"CVPR 2022 1","authors":["Boyun Li","Xiao Liu","Peng Hu","Zhongqin Wu","Jiancheng Lv","Xi Peng"],"abstract":"    In this paper, we study a challenging problem in image restoration, namely, how to develop an all-in-one method that could recover images from a variety of unknown corruption types and levels. To this end, we propose an All-in-one Image Restoration Network (AirNet) consisting of two neural modules, named Contrastive-Based Degraded Encoder (CBDE) and Degradation-Guided Restoration Network (DGRN). The major advantages of AirNet are two-fold. First, it is an all-in-one solution which could recover various degraded images in one network. Second, AirNet is free from the prior of the corruption types and levels, which just uses the observed corrupted image to perform inference. These two advantages enable AirNet to enjoy better flexibility and higher economy in real world scenarios wherein the priors on the corruptions are hard to know and the degradation will change with space and time. Extensive experimental results show the proposed method outperforms 17 image restoration baselines on four challenging datasets. The code is available at https://github.com/XLearning-SCU/2022-CVPR-AirNet.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2022/html/Li_All-in-One_Image_Restoration_for_Unknown_Corruption_CVPR_2022_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2022/papers/Li_All-in-One_Image_Restoration_for_Unknown_Corruption_CVPR_2022_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"all-in-one-image-restoration-for-unknown","repo_url":"https://github.com/xlearning-scu/2022-cvpr-airnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"5-degradation-blind-all-in-one-image","task_name":"5-Degradation Blind All-in-One Image Restoration"},{"task_slug":"all","task_name":"All"},{"task_slug":"blind-all-in-one-image-restoration","task_name":"Blind All-in-One Image Restoration"},{"task_slug":"image-restoration","task_name":"Image Restoration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/5-degradation-blind-all-in-one-image","task":"5-Degradation Blind All-in-One Image Restoration","dataset":"5-Degradation Blind All-in-One Image Restoration","model":"AirNet","rank_in_archive_order":7,"of":7,"metrics":{"Average PSNR":"25.49"},"uses_additional_data":false},{"leaderboard":"/sota/blind-all-in-one-image-restoration-on-3","task":"Blind All-in-One Image Restoration","dataset":"3-Degradations","model":"AirNet","rank_in_archive_order":9,"of":9,"metrics":{"Average PSNR":"31.20","SSIM":"0.910"},"uses_additional_data":false},{"leaderboard":"/sota/blind-all-in-one-image-restoration-on-5","task":"Blind All-in-One Image Restoration","dataset":"5-Degradations","model":"AirNet","rank_in_archive_order":9,"of":9,"metrics":{"Average PSNR":"25.49","SSIM":"0.846"},"uses_additional_data":false},{"leaderboard":"/sota/image-restoration-on-cdd-11","task":"Image Restoration","dataset":"CDD-11","model":"AirNet","rank_in_archive_order":12,"of":14,"metrics":{"Average PSNR (dB)":"23.75","SSIM":"0.8140"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}