Low-Light Image Enhancement with Retinex Theory Optimized by Improved Whale Optimization Algorithm

Low-Light Image Enhancement with Retinex Theory Optimized by Improved Whale Optimization Algorithm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Low-Light Image Enhancement with Retinex Theory Optimized by Improved Whale Optimization Algorithm Zhiguo Zhang, Zhenchao Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8454761/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract To solve the problems of insufficient contrast, blurred details, and color distortion in low-light image Enhancement, this paper proposes a Low-Light Image Enhancement with Retinex Theory Optimized by Improved Whale Optimization Algorithm(WOA). The method innovatively employs Cauchy chaos initialization to enhance the population diversity and global search capability of the WOA population, while introducing adaptive weight adjustment and Gaussian perturbation strategies to improve the local search precision of WOA. By dynamically searching the optimal combination of Retinex parameters—including Gaussian kernel standard deviations ( \(\:\sigma\:\) ₁ , \(\:\sigma\:\) ₂ , \(\:\sigma\:\) ₃ ), brightness coefficient, bias (b), and color factor—through the improved WOA, a multi-metric joint optimization model is constructed for intelligent tuning of low-light enhancement parameters. Experimental validation on the LOL dataset demonstrates that compared to several traditional image enhancement algorithms (automatic white balance, adaptive contrast enhancement, histogram equalization) and the deep learning-based ZeroDCE method, the proposed approach significantly improves image clarity and contrast while effectively enhancing key metrics such as information entropy (IE), average gradient (AG), and perceptual quality indices (NIQE and BRISQUE). Ablation studies further quantify the contributions of individual improvements: Gaussian perturbation accounts for approximately 79.2% of performance enhancement, adaptive weight adjustment contributes 19.6%, while Cauchy chaotic initialization shows minimal impact (1.2%) in the current parameter configuration. The proposed WOA-Retinex method effectively balances natural appearance preservation with detail enhancement in low-light imaging scenarios. Improved whale optimization algorithm Retinex algorithm Multi-metric joint optimization model Cauchy chaos initialization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Feb, 2026 Reviews received at journal 05 Feb, 2026 Reviewers agreed at journal 06 Jan, 2026 Reviewers invited by journal 06 Jan, 2026 Editor assigned by journal 27 Dec, 2025 Submission checks completed at journal 27 Dec, 2025 First submitted to journal 26 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8454761","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":572660230,"identity":"b32f813d-1bd0-4ca0-a08f-2e9aac0971ae","order_by":0,"name":"Zhiguo Zhang","email":"","orcid":"","institution":"China Mobile Internet of Things Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Zhiguo","middleName":"","lastName":"Zhang","suffix":""},{"id":572660231,"identity":"ccb08164-949d-45fa-9580-aa9d22c3da1b","order_by":1,"name":"Zhenchao 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Algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Improved whale optimization algorithm, Retinex algorithm, Multi-metric joint optimization model, Cauchy chaos initialization","lastPublishedDoi":"10.21203/rs.3.rs-8454761/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8454761/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo solve the problems of insufficient contrast, blurred details, and color distortion in low-light image Enhancement, this paper proposes a Low-Light Image Enhancement with Retinex Theory Optimized by Improved Whale Optimization Algorithm(WOA). The method innovatively employs Cauchy chaos initialization to enhance the population diversity and global search capability of the WOA population, while introducing adaptive weight adjustment and Gaussian perturbation strategies to improve the local search precision of WOA. By dynamically searching the optimal combination of Retinex parameters\u0026mdash;including Gaussian kernel standard deviations (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e₁\u003c/em\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e₂\u003c/em\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e₃\u003c/em\u003e), brightness coefficient, bias (b), and color factor\u0026mdash;through the improved WOA, a multi-metric joint optimization model is constructed for intelligent tuning of low-light enhancement parameters. Experimental validation on the LOL dataset demonstrates that compared to several traditional image enhancement algorithms (automatic white balance, adaptive contrast enhancement, histogram equalization) and the deep learning-based ZeroDCE method, the proposed approach significantly improves image clarity and contrast while effectively enhancing key metrics such as information entropy (IE), average gradient (AG), and perceptual quality indices (NIQE and BRISQUE). Ablation studies further quantify the contributions of individual improvements: Gaussian perturbation accounts for approximately 79.2% of performance enhancement, adaptive weight adjustment contributes 19.6%, while Cauchy chaotic initialization shows minimal impact (1.2%) in the current parameter configuration. The proposed WOA-Retinex method effectively balances natural appearance preservation with detail enhancement in low-light imaging scenarios.\u003c/p\u003e","manuscriptTitle":"Low-Light Image Enhancement with Retinex Theory Optimized by Improved Whale Optimization Algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 05:46:07","doi":"10.21203/rs.3.rs-8454761/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-08T03:28:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-05T09:31:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148192765895366312257699381180742197946","date":"2026-01-07T04:47:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-06T20:05:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-27T05:02:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-27T05:01:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Signal, Image and Video Processing","date":"2025-12-26T10:49:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"292d78ad-d8a6-4bd2-afe9-c8f6d7992fcb","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-10T02:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 05:46:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8454761","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8454761","identity":"rs-8454761","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}