A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement

A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement | 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 Article A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement Muhammad Azeem Aslam, Hassan Khalid, Nisar Ahmed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6819288/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Low-light image enhancement remains a challenging task, particularly in the absence of paired training data. In this study, we present LucentVisionNet, a novel zero-shot learning framework that addresses the limitations of traditional and deep learning-based enhancement methods. The proposed approach integrates multi-scale spatial attention with a deep curve estimation network, enabling fine-grained enhancement while preserving semantic and perceptual fidelity. To further improve generalization, we adopt a recurrent enhancement strategy and optimize the model using a composite loss function comprising six tailored components, including a novel no-reference image quality loss inspired by human visual perception. Extensive experiments on both paired and unpaired benchmark datasets demonstrate that LucentVisionNet consistently outperforms state-of-the-art supervised, unsupervised, and zero-shot methods across multiple full-reference and no-reference image quality metrics. Our framework achieves high visual quality, structural consistency, and computational efficiency, making it well-suited for deployment in real-world applications such as mobile photography, surveillance, and autonomous navigation. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 Sep, 2025 Reviews received at journal 25 Aug, 2025 Reviewers agreed at journal 24 Aug, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviews received at journal 08 Jul, 2025 Reviewers agreed at journal 07 Jul, 2025 Reviewers invited by journal 24 Jun, 2025 Editor invited by journal 06 Jun, 2025 Editor assigned by journal 06 Jun, 2025 Submission checks completed at journal 05 Jun, 2025 First submitted to journal 04 Jun, 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. 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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-6819288","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":475790726,"identity":"65527e84-b45e-44de-8de5-41364a37d50d","order_by":0,"name":"Muhammad Azeem Aslam","email":"","orcid":"","institution":"Xi'an Eurasia University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Azeem","lastName":"Aslam","suffix":""},{"id":475790727,"identity":"ef97a58a-c922-463c-b1fa-d6324a578d7d","order_by":1,"name":"Hassan Khalid","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYLACxgYIfeBDBZBkZm4gWgvjwxlnQFpgJhChhdmYtw3ZUhyAX7r92gfGHXb28u1nj0nzzjsczd8O1PKjYhtOLZJzzhTPYDyTnLjhTF6a5Nxth3NnHGZsYOw5cxunFoMbOckMjG3MCQYMOWYSb4FaGoBamBnbcGuxh2ipt5fvf2MmwTvncO58QloMJNIPA7UAld3IMTbkbTicu4GQFokbOcxALccTN9x4Y/hwxrH03I1ALQfx+YV/RvpjoJZqoMNyDA58qLHOnXf+8MEHPypwa2Fg4DFg/oPgNYPJA3jUAwH7A2ReHX7Fo2AUjIJRMCIBAKzmXToJhQVPAAAAAElFTkSuQmCC","orcid":"","institution":"University of Engineering and Technology Lahore","correspondingAuthor":true,"prefix":"","firstName":"Hassan","middleName":"","lastName":"Khalid","suffix":""},{"id":475790728,"identity":"acdede30-5c60-488c-b318-f5ae60663358","order_by":2,"name":"Nisar Ahmed","email":"","orcid":"","institution":"University of Engineering and Technology Lahore","correspondingAuthor":false,"prefix":"","firstName":"Nisar","middleName":"","lastName":"Ahmed","suffix":""}],"badges":[],"createdAt":"2025-06-04 10:23:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6819288/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6819288/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-30479-3","type":"published","date":"2025-12-03T15:57:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":97723933,"identity":"2fd297d1-7714-4b1e-977a-9de0c2f9fca5","added_by":"auto","created_at":"2025-12-08 16:09:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2185757,"visible":true,"origin":"","legend":"","description":"","filename":"LowLightImageEnhancement.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6819288/v1_covered_68c05bee-f3ad-431f-bf41-831227504e7d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6819288/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6819288/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Low-light image enhancement remains a challenging task, particularly in the absence of paired training data. 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