SCD-IGNet: Enhanced Semantic Segmentation of Low-Light Rainy Images via Symmetric Cross-Decoupling and Illumination Guidance

SCD-IGNet: Enhanced Semantic Segmentation of Low-Light Rainy Images via Symmetric Cross-Decoupling and Illumination Guidance | 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 SCD-IGNet: Enhanced Semantic Segmentation of Low-Light Rainy Images via Symmetric Cross-Decoupling and Illumination Guidance Xiaochun Lei, Yingjian Liu, Zetao Jiang, Weilin Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6382248/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Semantic segmentation, a critical research area in computer vision, is essential for various applications, including autonomous driving and intelligent transportation. However, existing semantic segmentation methods often perform poorly in low-light rainy conditions due to challenges such as underexposure, occlusion, and rain streak noise. To address these issues, we propose a novel semantic segmentation method for low-light rainy images, named SCD-IGNet. This method leverages symmetric cross-decoupling and illumination guidance to enhance segmentation accuracy. SCD-IGNet decouples low-light rainy images into content and style components using a decoupling enhancement module, which comprises a global and local feature extraction module and a differential content enhancement module. The content components are then processed by a backbone network to extract features, while the style components generate illumination features that aid in semantic segmentation. Experimental results demonstrate that our method achieves a 1.1% improvement in mean intersection over union and a 2.1% increase in mean accuracy compared to the baseline method on the NightCity-rain dataset. Additionally, our method exhibits excellent performance on the NightCity-fine dataset, validating its efficacy for low-light rainy image semantic segmentation. The code is available at https://github.com/Liusir765832/SCD-IGNet.git . Semantic Segmentation of Low-Light Rainy Images Style Transfer Multi-Scale Selective Fusion Image Denoising Content Enhancement Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 09 Dec, 2025 Reviews received at journal 05 Oct, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviews received at journal 03 Aug, 2025 Reviewers agreed at journal 23 Jul, 2025 Reviewers agreed at journal 08 May, 2025 Reviewers invited by journal 06 May, 2025 Editor assigned by journal 07 Apr, 2025 Submission checks completed at journal 07 Apr, 2025 First submitted to journal 05 Apr, 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-6382248","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453856962,"identity":"1aff97b3-60bd-4438-b95e-b70abe643cda","order_by":0,"name":"Xiaochun Lei","email":"","orcid":"","institution":"Guilin Universityof Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaochun","middleName":"","lastName":"Lei","suffix":""},{"id":453856974,"identity":"5e595f04-1c30-4dc3-81b7-d7b70c93a427","order_by":1,"name":"Yingjian Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACfvkD6T8S/0jI8TMzHyBOi+QMhgcSHxssjCXb2xKI02Jwg/GB5MyGisQNPWcMiHTZ7eYEY94dEokbJHI+3njDYCen20BAB+OcYwnJvGckjLdL5G62nMOQbGx2gIAWZoachMM8bBKyO2fkbpPmYTiQuI2QFjaG/I/NQC2MG27kPCNOC49EQjLjzDYJxQ1nzrARp0WC50AawwegX4CBbGw5x4AIv9gfb0hjSKioA0XlwxtvKuzkCGpBs5LYqEHSQqqOUTAKRsEoGBEAAC6CRUIMlpAkAAAAAElFTkSuQmCC","orcid":"","institution":"Guilin Universityof Electronic Technology","correspondingAuthor":true,"prefix":"","firstName":"Yingjian","middleName":"","lastName":"Liu","suffix":""},{"id":453856976,"identity":"33b3e521-0c16-442b-b600-852dc0d423cc","order_by":2,"name":"Zetao Jiang","email":"","orcid":"","institution":"Guilin Universityof Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Zetao","middleName":"","lastName":"Jiang","suffix":""},{"id":453856979,"identity":"2dccb5c0-6539-4e86-89bf-d23a214dbd53","order_by":3,"name":"Weilin Wu","email":"","orcid":"","institution":"Guilin Universityof Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Weilin","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-04-05 13:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6382248/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6382248/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82320472,"identity":"2f30b83e-7ff2-42f1-827f-ade284c24db6","added_by":"auto","created_at":"2025-05-09 04:51:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":718088,"visible":true,"origin":"","legend":"","description":"","filename":"SCDIGNetThevisualcomputer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6382248/v1_covered_cb59627d-d7cd-4f58-8ebf-bd40c463d060.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SCD-IGNet: Enhanced Semantic Segmentation of Low-Light Rainy Images via Symmetric Cross-Decoupling and Illumination Guidance","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":"the-visual-computer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tvcj","sideBox":"Learn more about [The Visual Computer](http://link.springer.com/journal/371)","snPcode":"371","submissionUrl":"https://submission.nature.com/new-submission/371/3","title":"The Visual Computer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Semantic Segmentation of Low-Light Rainy Images, Style Transfer; Multi-Scale Selective Fusion, Image Denoising, Content Enhancement","lastPublishedDoi":"10.21203/rs.3.rs-6382248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6382248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSemantic segmentation, a critical research area in computer vision, is essential for various applications, including autonomous driving and intelligent transportation. However, existing semantic segmentation methods often perform poorly in low-light rainy conditions due to challenges such as underexposure, occlusion, and rain streak noise. To address these issues, we propose a novel semantic segmentation method for low-light rainy images, named SCD-IGNet. This method leverages symmetric cross-decoupling and illumination guidance to enhance segmentation accuracy. SCD-IGNet decouples low-light rainy images into content and style components using a decoupling enhancement module, which comprises a global and local feature extraction module and a differential content enhancement module. The content components are then processed by a backbone network to extract features, while the style components generate illumination features that aid in semantic segmentation. 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