Enhancing Blind Image Deblurring Robustness Against Impulse Noise via Adaptive Noise Detection and Graph Regularization

Enhancing Blind Image Deblurring Robustness Against Impulse Noise via Adaptive Noise Detection and Graph Regularization | 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 Enhancing Blind Image Deblurring Robustness Against Impulse Noise via Adaptive Noise Detection and Graph Regularization Xin Liu, Zhe Li, Libo Cheng, Xue Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6284804/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jun, 2025 Read the published version in Signal, Image and Video Processing → Version 1 posted 10 You are reading this latest preprint version Abstract Blind image deblurring is a challenging task aimed at recovering latent images from blurred observations. The presence of impulse noise significantly complicates this process by degrading image details and edge information, adversely affecting kernel estimation and image restoration. To address this issue, we propose a robust blind image deblurring algorithm that integrates adaptive impulse noise detection. Our approach employs an adaptive noise detection method to construct a noise mask matrix, which is then embedded into a graph-regularized blur kernel restoration model. This integration mitigates the detrimental effects of impulse noise on kernel estimation. Furthermore, we introduce a noise weight matrix correction term into the latent clean image restoration process to enhance the accuracy of noise handling. Experimental results demonstrate that our algorithm outperforms existing methods, achieving higher PSNR and SSIM values for restored images under various impulse noise densities. Our work not only advances the state-of-the-art in blind image deblurring but also provides a practical solution for handling impulse noise in real-world applications.The code files can be downloaded from " https://github.com/Edith0000/Enhancing-Blind-Image-Deblurring-Robustness-Against-Impulse-Noise ". Blind Image Deblurring Impulse Noise Detection Adaptive Restoration Algorithm Non-convex Optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Jun, 2025 Read the published version in Signal, Image and Video Processing → Version 1 posted Editorial decision: Revision requested 06 May, 2025 Reviews received at journal 05 May, 2025 Reviewers agreed at journal 15 Apr, 2025 Reviews received at journal 15 Apr, 2025 Reviewers agreed at journal 13 Apr, 2025 Reviewers agreed at journal 13 Apr, 2025 Reviewers agreed at journal 13 Apr, 2025 Reviewers invited by journal 13 Apr, 2025 Submission checks completed at journal 12 Apr, 2025 First submitted to journal 11 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. 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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-6284804","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445041145,"identity":"9714296b-a7e0-4e6f-bb17-486231255137","order_by":0,"name":"Xin Liu","email":"","orcid":"","institution":"Changchun University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Liu","suffix":""},{"id":445041147,"identity":"2a0dab7d-d200-4c46-9086-0dbda55bcbd6","order_by":1,"name":"Zhe Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie2RMQrCQBBFNyxsFUg7ATFXWBBSeZhZAqkiWkmKFIKSFCK2ET2ElViuRFKtfcrkBtppIyqWShI7i331POb/GUI0mn8EiCExBNOi9FBiGLVSiETV79pJ7PFS5S0VI/Z7XCnXrma02XDWUylLlolVGrihmDBiJXOsVYxNjhLNTCzBHxdi3yGgTttahULAJcJri7crhGKEw6BeYTA8S+SZ2BbojkRMmxUTgufF8FX/6JJWCoD/DCbfRwZUudnYxUm9qrrd36+8XMOoayWLeuUz6W/jGo1Go/nKA3PjUI7zpEZQAAAAAElFTkSuQmCC","orcid":"","institution":"Changchun University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Li","suffix":""},{"id":445041148,"identity":"f89a36c0-377c-4956-b1db-26675302fd20","order_by":2,"name":"Libo Cheng","email":"","orcid":"","institution":"Changchun University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Libo","middleName":"","lastName":"Cheng","suffix":""},{"id":445041149,"identity":"89058401-aca9-481f-a03e-157dda6bd09b","order_by":3,"name":"Xue Jiang","email":"","orcid":"","institution":"Changchun University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2025-03-22 16:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6284804/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6284804/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11760-025-04333-y","type":"published","date":"2025-06-13T15:57:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84726563,"identity":"ad19bdbb-f927-4ff1-b758-2a4e64d2ba2b","added_by":"auto","created_at":"2025-06-16 16:07:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7583033,"visible":true,"origin":"","legend":"","description":"","filename":"Submit2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6284804/v1_covered_f0b0ff0c-a34b-4d30-bc7d-f1dcdb15e828.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Blind Image Deblurring Robustness Against Impulse Noise via Adaptive Noise Detection and Graph Regularization","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":"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":" Blind Image Deblurring, Impulse Noise Detection, Adaptive Restoration Algorithm, Non-convex Optimization","lastPublishedDoi":"10.21203/rs.3.rs-6284804/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6284804/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Blind image deblurring is a challenging task aimed at recovering latent images from blurred observations. 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