{"url":"/sota/5-degradation-blind-all-in-one-image","task":{"name":"5-Degradation Blind All-in-One Image Restoration","url":"/task/5-degradation-blind-all-in-one-image","note":null},"dataset":{"name":"5-Degradation Blind All-in-One Image Restoration","url":null},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"Blind All-in-One Image Restoration aims to remove various degradations from an input image without prior knowledge of the degradation type or severity. In this task, we include 5 of the most common image restoration tasks with five degradations: rain, haze, noise, blur, and low-light conditions. This task focuses on five common image restoration tasks, each addressing a specific degradation: rain , haze, noise, blur, and low-light conditions. For training, we utilize the following datasets: Rain200L for deraining, RESIDE for dehazing, WED and BSD400 for denoising with a noise level of σ=25, GoPro for deblurring, and LoLv1 for low-light enhancement. For evaluation, we employ: Rain100L for deraining, SOTS (outdoor) for dehazing, BSD68 for denoising with σ=25, GoPro for deblurring, and LoLv1 for low-light enhancement. The performance of the models is assessed by reporting the average PSNR across all five evaluation datasets, reflecting the overall capability of the model to handle diverse degradations.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Average PSNR","LPIPS"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Average PSNR":"higher","LPIPS":null}},"counts":{"rows":7,"rows_with_code":6,"rows_with_paper_page":7,"rows_dated":7,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"ABAIR","metrics":{"Average PSNR":"31.25"},"uses_additional_data":false,"paper_date":"2024-11-27","paper":"/paper/adaptive-blind-all-in-one-image-restoration","paper_url":"https://arxiv.org/abs/2411.18412v1","paper_title":"Adaptive Blind All-in-One Image Restoration","code":"https://github.com/davidserra9/abair","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"DA-RCOT","metrics":{"Average PSNR":"30.40","LPIPS":"0.064"},"uses_additional_data":false,"paper_date":"2024-11-03","paper":"/paper/degradation-aware-residual-conditioned","paper_url":"https://arxiv.org/abs/2411.01656v1","paper_title":"Degradation-Aware Residual-Conditioned Optimal Transport for Unified Image Restoration","code":"https://github.com/xl-tang3/RCOT","n_code_links":2,"syntology":{"n_ran":6,"n_unverified":0,"n_samples":6,"n_pointer_only_licence":6}},{"rank_in_archive_order":3,"model":"HAIR","metrics":{"Average PSNR":"30.37"},"uses_additional_data":false,"paper_date":"2024-08-15","paper":"/paper/hair-hypernetworks-based-all-in-one-image","paper_url":"https://arxiv.org/abs/2408.08091v4","paper_title":"HAIR: Hypernetworks-based All-in-One Image Restoration","code":"https://github.com/toummHus/HAIR","n_code_links":1,"syntology":{"n_ran":13,"n_unverified":5,"n_samples":18,"n_pointer_only_licence":18}},{"rank_in_archive_order":4,"model":"DaAIR","metrics":{"Average PSNR":"30.24"},"uses_additional_data":false,"paper_date":"2024-05-24","paper":"/paper/efficient-degradation-aware-any-image","paper_url":"https://arxiv.org/abs/2405.15475v2","paper_title":"Efficient Degradation-aware Any Image Restoration","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":5,"model":"AnyIR","metrics":{"Average PSNR":"29.65"},"uses_additional_data":false,"paper_date":"2024-07-18","paper":"/paper/any-image-restoration-with-efficient","paper_url":"https://arxiv.org/abs/2407.13372v2","paper_title":"Restore Anything Model via Efficient Degradation Adaptation","code":"https://github.com/Amazingren/AnyIR","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"IDR","metrics":{"Average PSNR":"28.34"},"uses_additional_data":false,"paper_date":"2023-01-01","paper":"/paper/ingredient-oriented-multi-degradation","paper_url":"http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Ingredient-Oriented_Multi-Degradation_Learning_for_Image_Restoration_CVPR_2023_paper.html","paper_title":"Ingredient-Oriented Multi-Degradation Learning for Image Restoration","code":"https://github.com/JingHao99/IDR-Ingredients-oriented-Degradation-Reformulation","n_code_links":1,"syntology":null},{"rank_in_archive_order":7,"model":"AirNet","metrics":{"Average PSNR":"25.49"},"uses_additional_data":false,"paper_date":"2022-01-01","paper":"/paper/all-in-one-image-restoration-for-unknown","paper_url":"http://openaccess.thecvf.com//content/CVPR2022/html/Li_All-in-One_Image_Restoration_for_Unknown_Corruption_CVPR_2022_paper.html","paper_title":"All-in-One Image Restoration for Unknown Corruption","code":"https://github.com/xlearning-scu/2022-cvpr-airnet","n_code_links":1,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,795 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6795,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":2785},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. 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