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Opt."],"published-print":{"date-parts":[[2026,3,10]]},"abstract":"<jats:p>Underwater images often exhibit color distortion and low illumination\n\t\t\t\t\tbecause of the complex imaging mechanism of the underwater scene.\n\t\t\t\t\tThese issues can significantly hinder the performance of underwater\n\t\t\t\t\tvision applications, including object segmentation and detection. To\n\t\t\t\t\tsolve these limitations, we propose FPSANet, an innovative feature\n\t\t\t\t\tpyramid serial attention network designed for underwater image\n\t\t\t\t\tenhancement. This framework leverages multiscale feature fusion and an\n\t\t\t\t\tadvanced attention mechanism. Initially, we propose a feature pyramid\n\t\t\t\t\tfusion module to integrate spatial information across multiple scales.\n\t\t\t\t\tSubsequently, we design a serial attention module (SAM) that\n\t\t\t\t\tprioritizes illumination features and emphasizes critical color\n\t\t\t\t\tdetails by combining with the pixel, channel, and space attention.\n\t\t\t\t\tMoreover, both qualitative analysis and quantitative evaluations\n\t\t\t\t\treveal that our method excels across diverse underwater datasets,\n\t\t\t\t\ti.e.,\u00a0compared with the second-best comparative method, our\n\t\t\t\t\tmethod increases by 3.23% and 1.46%, at least in terms of the PSNR and\n\t\t\t\t\tSSIM values, respectively. The experimental results highlight its\n\t\t\t\t\teffectiveness in tasks such as image segmentation, keypoint detection,\n\t\t\t\t\tand even the enhancement of foggy images.<\/jats:p>","DOI":"10.1364\/ao.583911","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T20:00:10Z","timestamp":1770667210000},"page":"2721","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Feature pyramid serial attention network for underwater image enhancement"],"prefix":"10.1364","volume":"65","author":[{"given":"Jie","family":"Shen","sequence":"first","affiliation":[{"name":"Chizhou University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Xu","sequence":"additional","affiliation":[{"name":"Anhui University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Hefei Financial China Information & Technology Co., Ltd."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinqin","family":"Zhong","sequence":"additional","affiliation":[{"name":"Anhui University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8234-5165","authenticated-orcid":true,"given":"Zheng","family":"Liang","sequence":"additional","affiliation":[{"name":"Anhui University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Ju","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/05th6yx34","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,3,5]]},"reference":[{"key":"ao-65-8-2721-R1","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1109\/LRA.2017.2730363","type":"journal-article","volume":"3","author":"Li","year":"2018","journal-title":"IEEE Rob.\n\t\t\t\t\tAutom. 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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"}]}}