FD-Vision mamba for endoscopic Exposure Correction

FD-Vision mamba for endoscopic Exposure Correction | 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 FD-Vision mamba for endoscopic Exposure Correction Pu Wang, Xin Su, Fang Wang, Chenggang Shan, Zhuoran Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6847505/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract In endoscopic imaging, the recorded images are prone to exposure abnormalities, so maintaining high-quality images is important to assist healthcare professionals in performing decision-making. To overcome this issue, We design a frequency-domain-based network, called FD-Vision Mamba (FDVM-Net), which achieves high-quality image exposure correction by reconstructing the frequency domain of endoscopic images. Specifically, inspired by the State Space Sequence Models (SSMs), we develop a C-SSM block that integrates the local feature extraction ability of the convolutional layer with the ability of the SSM to capture long-range dependencies. Then, a dual-path network is built using C-SSM as the basic function cell, and these two paths deal with the phase and amplitude information of the image, respectively. Finally, a degraded endoscopic image is reconstructed by FDVM-Net to obtain a high-quality clear image. Extensive experimental results demonstrate that our method achieves state-of-the-art results in terms of speed and accuracy, and it is noteworthy that our method can enhance endoscopic images of arbitrary resolution. Endoscopic imaging Frequency domain FD-Vision Mamba Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Jun, 2025 Reviewers invited by journal 11 Jun, 2025 Editor assigned by journal 11 Jun, 2025 Submission checks completed at journal 11 Jun, 2025 First submitted to journal 08 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. 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-6847505","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470094325,"identity":"d795a289-6033-4064-8d60-1b7487dabb4c","order_by":0,"name":"Pu Wang","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Pu","middleName":"","lastName":"Wang","suffix":""},{"id":470094326,"identity":"ad0d6884-96d4-42b0-b1d7-48fe08bd5855","order_by":1,"name":"Xin Su","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Su","suffix":""},{"id":470094327,"identity":"0231be30-54a0-405f-98c1-87e19bd42674","order_by":2,"name":"Fang Wang","email":"","orcid":"","institution":"Zaozhuang University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Wang","suffix":""},{"id":470094328,"identity":"4f3617f8-80b1-464a-bade-eba4116102e0","order_by":3,"name":"Chenggang Shan","email":"","orcid":"","institution":"Zaozhuang University","correspondingAuthor":false,"prefix":"","firstName":"Chenggang","middleName":"","lastName":"Shan","suffix":""},{"id":470094329,"identity":"a4ffcc68-afd8-4ae3-a42d-c8c2e2cd4e31","order_by":4,"name":"Zhuoran Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDACCQglx8BwAMxgbCBWizFYywFStCSCVRKlRX5287GHX/7cS9/OeMb48wcGG9kNB5ifPcCnhXHOsXRjGZ7i3J0NZ8wkDjCkGW84wGZugE8Ls0SOmbSERELuhgNnzIAOO5y44QAPmwQ+LWwS+d+kJQwS0g0OnDH+cIDhP2EtPBI5bJIfEhISgFoMgA47QFiLhESamTTDgQTDDQeOlUmcMUg2nnmYzQyvFvkZyc8kf/xJkDe4cXjzh4oKO9m+483P8GoBAWYesH0HgAQoqJgJqQcCxh8gkr+BCKWjYBSMglEwIgEAMRBMFWKZa+UAAAAASUVORK5CYII=","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Zhuoran","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2025-06-08 12:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6847505/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6847505/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84670784,"identity":"2014bedf-49e6-4f86-9486-0015eb7ef228","added_by":"auto","created_at":"2025-06-16 06:38:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2302842,"visible":true,"origin":"","legend":"","description":"","filename":"snarticletemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6847505/v1_covered_c02dbc90-783f-4040-8f02-0780f06b90a7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"FD-Vision mamba for endoscopic Exposure Correction","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":"Endoscopic imaging, Frequency domain, FD-Vision Mamba","lastPublishedDoi":"10.21203/rs.3.rs-6847505/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6847505/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In endoscopic imaging, the recorded images are prone to exposure abnormalities, so maintaining high-quality images is important to assist healthcare professionals in performing decision-making. To overcome this issue, We design a frequency-domain-based network, called FD-Vision Mamba (FDVM-Net), which achieves high-quality image exposure correction by reconstructing the frequency domain of endoscopic images. Specifically, inspired by the State Space Sequence Models (SSMs), we develop a C-SSM block that integrates the local feature extraction ability of the convolutional layer with the ability of the SSM to capture long-range dependencies. Then, a dual-path network is built using C-SSM as the basic function cell, and these two paths deal with the phase and amplitude information of the image, respectively. Finally, a degraded endoscopic image is reconstructed by FDVM-Net to obtain a high-quality clear image. 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