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Opt."],"published-print":{"date-parts":[[2026,2,20]]},"abstract":"<jats:p>Removing scattering is a central challenge in underwater imaging\n\t\t\t\t\tbecause water induces\n\t\t\t\t\tcomplex light\u2013matter interactions. Scattering not only blurs image\n\t\t\t\t\tdetails but also degrades the reliability of downstream vision tasks.\n\t\t\t\t\tImportantly, backscattered light and object-reflected light exhibit\n\t\t\t\t\tdistinct polarimetric differences, offering a physical cue for\n\t\t\t\t\tdescattering. However, most existing approaches usually rely on a\n\t\t\t\t\tsingle polarization parameter or assume a constant background\n\t\t\t\t\tpolarization degree, limiting their effectiveness in complex scenes.\n\t\t\t\t\tTherefore, an underwater image enhancement network based on a\n\t\t\t\t\tdiffusion model and a polarization drive is proposed, which decomposes\n\t\t\t\t\tthe problem into two stages: (i)\u00a0descattering prior generation\n\t\t\t\t\tand (ii)\u00a0scattering removal. In the prior stage, the diffusion\n\t\t\t\t\tmodel is introduced for the first time, to our knowledge, to fully\n\t\t\t\t\tmine the polarization information and distill it into high-quality\n\t\t\t\t\tpriors that guide accurate scattering suppression. In the removal\n\t\t\t\t\tstage, the illumination estimation module is designed to compensate\n\t\t\t\t\tfor water-body absorption and enforce global illumination consistency,\n\t\t\t\t\tthereby improving perceptual naturalness. Extensive experiments\n\t\t\t\t\tdemonstrate state-of-the-art quantitative and qualitative performance,\n\t\t\t\t\tproviding reliable, high-quality inputs for downstream underwater\n\t\t\t\t\tvision applications.<\/jats:p>","DOI":"10.1364\/ao.584547","type":"journal-article","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T14:00:10Z","timestamp":1769176810000},"page":"1800","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Polarization-guided diffusion model for physically inspired underwater image descattering"],"prefix":"10.1364","volume":"65","author":[{"given":"Luxiu","family":"Li","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03awzbc87","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyue","family":"Zhang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03awzbc87","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yubo","family":"Zheng","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03awzbc87","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyu","family":"Ru","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03awzbc87","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengdong","family":"Wu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03awzbc87","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"key":"ao-65-6-1800-R1","doi-asserted-by":"publisher","first-page":"2915","DOI":"10.1364\/AO.452318","type":"journal-article","volume":"61","author":"Zhou","year":"2022","journal-title":"Appl. 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Mach.\n\t\t\t\t\tIntell."},{"key":"ao-65-6-1800-R37","first-page":"25445","article-title":"Selective\n\t\t\t\t\thourglass mapping for universal image restoration based on diffusion\n\t\t\t\t\tmodel","volume-title":"Proceedings of the IEEE\/CVF\n\t\t\t\t\tConference on Computer Vision and Pattern Recognition","author":"Zheng","year":"2024"},{"key":"ao-65-6-1800-R38","doi-asserted-by":"publisher","first-page":"127585","DOI":"10.1016\/j.neucom.2024.127585","type":"journal-article","volume":"584","author":"Tolie","year":"2024","journal-title":"Neurocomputing"}],"container-title":["Applied Optics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/opg.optica.org\/viewmedia.cfm?URI=ao-65-6-1800&seq=0","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T14:22:15Z","timestamp":1778682135000},"score":1,"resource":{"primary":{"URL":"https:\/\/opg.optica.org\/abstract.cfm?URI=ao-65-6-1800"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,12]]},"references-count":38,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026]]},"published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.1364\/ao.584547","relation":{},"ISSN":["1559-128X","2155-3165"],"issn-type":[{"value":"1559-128X","type":"print"},{"value":"2155-3165","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,12]]},"assertion":[{"value":"Optica Publishing Group","name":"publisher","label":"This article is maintained by"},{"value":"https:\/\/doi.org\/10.1364\/AO.584547","name":"articlelink","label":"Crossref DOI link to publisher maintained version"},{"value":"research-article","name":"content_type","label":"Article type"},{"value":"Screened by Similarity Check","name":"cross_check","label":"Similarity check"},{"value":"Yes","order":0,"name":"peer_reviewed","label":"Peer reviewed","group":{"name":"peer_review","label":"Peer review"}},{"value":"Single blind","order":1,"name":"review_process","label":"Review process","group":{"name":"peer_review","label":"Peer review"}},{"value":"11 November 2025","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"22 January 2026","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"12 February 2026","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}},{"value":"\u00a9 2026 Optica Publishing Group. All rights, including\n\t\t\t\t\tfor text and data mining (TDM), Artificial Intelligence (AI) training,\n\t\t\t\t\tand similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}