A salt-and-pepper noise denoising method based on the neighborhood-controlled fuzzy rough set model

A salt-and-pepper noise denoising method based on the neighborhood-controlled fuzzy rough set model | 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 A salt-and-pepper noise denoising method based on the neighborhood-controlled fuzzy rough set model Xinyue Han, Bin Pang, Wei Yao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8982634/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 In image processing, denoising can provide high-quality data for subsequent processing. Current denoising methods can be categorized into three types: learningbased methods, model-based methods, and filtering-based methods. Although the first two methods excel at handling complex noise, traditional filtering methods remain advantageous in scenarios requiring high real-time performance and limited computational resources. In this paper, an adaptive fuzzy nonlinear filtering algorithm (NCFR algorithm) based on neighborhood-controlled fuzzy rough set model is proposed to effectively remove salt-and-pepper noise from images. The proposed NCFR algorithm introduces fuzzy membership functions to address the uncertainties and fuzziness between image pixels, and utilizes the noise-free pixels within an adaptive neighborhood to restore noisy pixels, thereby efficiently reducing noise while preserving image details. Experimental results demonstrate that the NCFR algorithm significantly improves image restoration quality under various noise densities. In particular, at high noise densities exceeding 95%, the algorithm shows superior performance in terms of PSNR and SSIM indices, better preserving structural characteristics and texture details of the images. Fuzzy rough set Noise removal Salt-and-pepper noise Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor assigned by journal 21 Mar, 2026 Submission checks completed at journal 28 Feb, 2026 First submitted to journal 26 Feb, 2026 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. 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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-8982634","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624984872,"identity":"3208be47-b7e7-4a7d-a355-56106764d45e","order_by":0,"name":"Xinyue Han","email":"","orcid":"","institution":"Beijing Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xinyue","middleName":"","lastName":"Han","suffix":""},{"id":624984876,"identity":"df200b91-5f8b-480a-8ce6-cffbcdc0428d","order_by":1,"name":"Bin Pang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYJACZgYDBh5+Zv6HDxIqaojXIiPZ3sNs8ODMMWK1MDDYGJw5wyb5sIWZsHJz9rOHXxcU3OFhuJF7rCKxgY2Bv707Aa8Wy568NOsZBs94GGfkpd1I3CHDIHHm7Aa8WgwO5JgZ8xgc5mGWSDC7kXiGjcFAIpeAlvNvIFrYgFoKEtuYidByI8f4MUgLD88ZMwaitFjOeGPGPAOoRYK9LVki4cwxHoJ+MefPMf5c8Oewvf1h5oMff1TUyPG39xJwGAMDmwSyAA9e5VAtzB8IqhoFo2AUjIKRDQDXYkgdfUJUXgAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Pang","suffix":""},{"id":624984877,"identity":"e85baaa0-c491-47ad-9182-d075fb9a70df","order_by":2,"name":"Wei Yao","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Yao","suffix":""}],"badges":[],"createdAt":"2026-02-27 02:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8982634/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8982634/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107440955,"identity":"72cd153f-fd1c-448b-8fd4-0a474380c1dc","added_by":"auto","created_at":"2026-04-21 13:58:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7301253,"visible":true,"origin":"","legend":"","description":"","filename":"snarticletemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8982634/v1_covered_c23d85d6-36fb-4760-8d7f-8aced560c064.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A salt-and-pepper noise denoising method based on the neighborhood-controlled fuzzy rough set model","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":"journal-of-mathematical-imaging-and-vision","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmiv","sideBox":"Learn more about [Journal of Mathematical Imaging and Vision](http://link.springer.com/journal/10851)","snPcode":"10851","submissionUrl":"https://submission.nature.com/new-submission/10851/3","title":"Journal of Mathematical Imaging and Vision","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Fuzzy rough set, Noise removal, Salt-and-pepper noise","lastPublishedDoi":"10.21203/rs.3.rs-8982634/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8982634/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn image processing, denoising can provide high-quality data for subsequent processing. 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