D2W-GAN: Wavelet based Attention-Infused GAN for Enhanced Image Raindrop Removal in Vision System

D2W-GAN: Wavelet based Attention-Infused GAN for Enhanced Image Raindrop Removal in Vision System | 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 D2W-GAN: Wavelet based Attention-Infused GAN for Enhanced Image Raindrop Removal in Vision System Muthukumar Balamurugan, Shivarama Holla K, Varun P Gopi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7767719/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Raindrop lenses and image aberration are key problems that arise in computer vision applications, such as autonomous driving, surveillance, and environmental monitoring cameras. In this restoration work, we present a revolutionary scale-integrative method for raindrop removal that uses the Dual-Tree Complex Wavelet Transform (DT-CWT) in conjunction with U shaped network (U-Net), attention mechanisms, and Generative Adversarial Networks (GANs), called D2W-GAN. Most of the current reconstruction methods lack effectiveness in removing raindrop related of distortions effectively while preserving the image quality. This paper shows that the DT-CWT can accurately capturing both the details of the high frequency information and the general structure of the low frequency information, which enables the model to effectively separate raindrop artifacts from the original image. The restoration quality is improved because of the attention mechanism assistance in the network identification of raindrop-affected blobs. The GAN framework is employed to further enhance the output of the model, to produce results that look natural and consistent. D2W-GAN outperforms current state-of-the-art methods in terms of restored image quality and structural similarity, as measured by PSNR and SSIM. Furthermore, our proposed method proved to be efficient in computational time which is suitable for real time applications. By leveraging wavelet transforms, attention-based learning, and adversarial completion, D2W-GAN sets a new standard in image restoration for complex, uncontrolled environments. Raindrop removal GAN Complex Wavelet Transform (CWT) Attention Block U shaped network Mask Generator Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7767719","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533082416,"identity":"a5139f6c-1c8a-44a9-a1aa-c78884beba39","order_by":0,"name":"Muthukumar Balamurugan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYHACNgjJ3nzgwAcwg6AOZogWPp5jiQ9ngLQwE6tFTiLH2JgHLEBAg+6M/GOPeSrs5Nh4zphJ2/zaJs/HzMD44WMObi1mN5LZjXnOJBuzsbeVSef23TZsY2Zglpy5Da8WNmnetgOJbTyHt0nn9txmBGphY+YlqOXfgfo2iQQzacue2/ZEamk4kMAmkWJszPDjdiJhLWcem0nOOZZs2AYK5N6G28ltzIzN+P1yPPGZxJsaO3n5dmBU/vhz23Z+e/PBDx/xaEEFjG1gsoFY9SDwhxTFo2AUjIJRMFIAABB9TZLok9vFAAAAAElFTkSuQmCC","orcid":"","institution":"National Institute of Technology Tiruchirappalli","correspondingAuthor":true,"prefix":"","firstName":"Muthukumar","middleName":"","lastName":"Balamurugan","suffix":""},{"id":533082417,"identity":"987d36da-171f-4781-bd5c-8dc8a297e68e","order_by":1,"name":"Shivarama Holla K","email":"","orcid":"","institution":"Continental Autonomous Mobility (India) Pvt. 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System","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Raindrop removal, GAN, Complex Wavelet Transform (CWT), Attention Block, U shaped network, Mask Generator","lastPublishedDoi":"10.21203/rs.3.rs-7767719/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7767719/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRaindrop lenses and image aberration are key problems that arise in computer vision applications, such as autonomous driving, surveillance, and environmental monitoring cameras. In this restoration work, we present a revolutionary scale-integrative method for raindrop removal that uses the Dual-Tree Complex Wavelet Transform (DT-CWT) in conjunction with U shaped network (U-Net), attention mechanisms, and Generative Adversarial Networks (GANs), called D2W-GAN. Most of the current reconstruction methods lack effectiveness in removing raindrop related of distortions effectively while preserving the image quality. This paper shows that the DT-CWT can accurately capturing both the details of the high frequency information and the general structure of the low frequency information, which enables the model to effectively separate raindrop artifacts from the original image. The restoration quality is improved because of the attention mechanism assistance in the network identification of raindrop-affected blobs. The GAN framework is employed to further enhance the output of the model, to produce results that look natural and consistent. D2W-GAN outperforms current state-of-the-art methods in terms of restored image quality and structural similarity, as measured by PSNR and SSIM. Furthermore, our proposed method proved to be efficient in computational time which is suitable for real time applications. By leveraging wavelet transforms, attention-based learning, and adversarial completion, D2W-GAN sets a new standard in image restoration for complex, uncontrolled environments.\u003c/p\u003e","manuscriptTitle":"D2W-GAN: Wavelet based Attention-Infused GAN for Enhanced Image Raindrop Removal in Vision System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-24 08:39:06","doi":"10.21203/rs.3.rs-7767719/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb3a1844-35fe-42d7-9aae-047af50c14c9","owner":[],"postedDate":"October 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-20T01:59:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-24 08:39:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7767719","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7767719","identity":"rs-7767719","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}