{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T08:08:02Z","timestamp":1783930082194,"version":"3.55.0"},"reference-count":55,"publisher":"Institution of Engineering and Technology (IET)","issue":"3","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306180"],"award-info":[{"award-number":["62306180"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CAAI Trans on Intel Tech"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Underwater image enhancement remains a critical challenge in computational vision due to complex distortions caused by wavelength\u2010dependent light absorption and scattering. This paper introduces CEDFNet, a novel two\u2010stage framework that leverages advanced computational intelligence techniques for robust and high\u2010fidelity underwater image restoration. The first stage integrates a Colour Equalisation Transformer (CET) to perform global colour correction by modelling long\u2010range dependencies and mitigating dominant hue distortions. The second stage combines a Residual Texture Modulation Adaptor (RTMA) with an Enhanced Bilateral Enhancement Decoder (EBED) to refine structural details and enhance local contrast through context\u2010aware and adaptive feature learning. Extensive evaluations on benchmark datasets including UIEBD, LSUI, and Colour\u2010Checker7 validate the superiority of CEDFNet over existing state\u2010of\u2010the\u2010art approaches. Quantitatively, CEDFNet achieves significant improvements across multiple perceptual and fidelity metrics such as PSNR, SSIM, FID, and LPIPS. Comprehensive ablation studies further confirm the complementary roles of CET, RTMA, and EBED, whereas parameter sensitivity analyses highlight the framework's robust and stable behaviour. By integrating transformer\u2010based global correction with task\u2010adaptive local enhancement, CEDFNet advances the frontier of underwater image restoration in the domain of computational intelligence. It generalises well across diverse imaging conditions and offers a lightweight and end\u2010to\u2010end solution suitable for real\u2010world deployment in marine robotics, inspection, and visual perception systems.<\/jats:p>","DOI":"10.1049\/cit2.70117","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:24:52Z","timestamp":1773833092000},"page":"709-725","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Progressive Colour Equalisation and Detail Refinement for Underwater Image Enhancement"],"prefix":"10.1049","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1048-4486","authenticated-orcid":false,"given":"Songbai","family":"Liu","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering Shenzhen University  Shenzhen 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