{"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/efficient-degradation-aware-any-image","title":"Efficient Degradation-aware Any Image Restoration","arxiv_id":"2405.15475","date":"2024-05-24","proceeding":null,"authors":["Eduard Zamfir","Zongwei Wu","Nancy Mehta","Danda Pani Paudel","Yulun Zhang","Radu Timofte"],"abstract":"Reconstructing missing details from degraded low-quality inputs poses a significant challenge. Recent progress in image restoration has demonstrated the efficacy of learning large models capable of addressing various degradations simultaneously. Nonetheless, these approaches introduce considerable computational overhead and complex learning paradigms, limiting their practical utility. In response, we propose \\textit{DaAIR}, an efficient All-in-One image restorer employing a Degradation-aware Learner (DaLe) in the low-rank regime to collaboratively mine shared aspects and subtle nuances across diverse degradations, generating a degradation-aware embedding. By dynamically allocating model capacity to input degradations, we realize an efficient restorer integrating holistic and specific learning within a unified model. Furthermore, DaAIR introduces a cost-efficient parameter update mechanism that enhances degradation awareness while maintaining computational efficiency. Extensive comparisons across five image degradations demonstrate that our DaAIR outperforms both state-of-the-art All-in-One models and degradation-specific counterparts, affirming our efficacy and practicality. The source will be publicly made available at https://eduardzamfir.github.io/daair/","url_abs":"https://arxiv.org/abs/2405.15475v2","url_pdf":"https://arxiv.org/pdf/2405.15475v2.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":[],"tasks":[{"task_slug":"5-degradation-blind-all-in-one-image","task_name":"5-Degradation Blind All-in-One Image Restoration"},{"task_slug":"blind-all-in-one-image-restoration","task_name":"Blind All-in-One Image Restoration"},{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"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":"DaAIR","rank_in_archive_order":4,"of":7,"metrics":{"Average PSNR":"30.24"},"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":"DaAIR","rank_in_archive_order":5,"of":9,"metrics":{"Average PSNR":"30.24","SSIM":"0.910"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2405.15475","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}