{"url":"/task/5-degradation-blind-all-in-one-image","name":"5-Degradation Blind All-in-One Image Restoration","slug":"5-degradation-blind-all-in-one-image","description_markdown":"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.","categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"derived"},"counts":{"papers_tagged":8,"papers_with_code":7,"benchmarks":1,"benchmark_tables_in_archive":1,"benchmark_tables_shown":1,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":0,"subtasks":0,"parent_tasks":1},"benchmarks":[{"leaderboard":"/sota/5-degradation-blind-all-in-one-image","slug":"5-degradation-blind-all-in-one-image","dataset":"5-Degradation Blind All-in-One Image Restoration","dataset_url":null,"rows_in_archive":7,"metrics":["Average PSNR","LPIPS"],"first_row_in_archive_order":{"model":"ABAIR","paper_title":"Adaptive Blind All-in-One Image Restoration","paper_url":"/paper/adaptive-blind-all-in-one-image-restoration","paper_date":"2024-11-27","arxiv_id":"2411.18412","code_links":[{"title":"davidserra9/abair","url":"https://github.com/davidserra9/abair"}],"syntology":null}}],"datasets":[],"subtasks":[],"parent_tasks":[{"url":"/task/image-restoration","name":"Image Restoration"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":7,"of":7,"tagged_in_all":8,"items":[{"url":"/paper/degradation-aware-residual-conditioned","title":"Degradation-Aware Residual-Conditioned Optimal Transport for Unified Image Restoration","date":"2024-11-03","arxiv_id":"2411.01656","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_unverified":0,"n_pointer_only":6}},{"url":"/paper/universal-image-restoration-pre-training-via","title":"Universal Image Restoration Pre-training via Degradation Classification","date":"2025-01-26","arxiv_id":"2501.15510","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_unverified":3,"n_pointer_only":8}},{"url":"/paper/adaptive-blind-all-in-one-image-restoration","title":"Adaptive Blind All-in-One Image Restoration","date":"2024-11-27","arxiv_id":"2411.18412","repositories_listed":1,"syntology":null},{"url":"/paper/hair-hypernetworks-based-all-in-one-image","title":"HAIR: Hypernetworks-based All-in-One Image Restoration","date":"2024-08-15","arxiv_id":"2408.08091","repositories_listed":1,"syntology":{"n":18,"n_ran":13,"n_unverified":5,"n_pointer_only":18}},{"url":"/paper/any-image-restoration-with-efficient","title":"Restore Anything Model via Efficient Degradation Adaptation","date":"2024-07-18","arxiv_id":"2407.13372","repositories_listed":1,"syntology":null},{"url":"/paper/ingredient-oriented-multi-degradation","title":"Ingredient-Oriented Multi-Degradation Learning for Image Restoration","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/all-in-one-image-restoration-for-unknown","title":"All-in-One Image Restoration for Unknown Corruption","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null}],"syntology_records":3,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}