Application of the Improved Restormer Model for Walnut X-ray Image Denoising

Application of the Improved Restormer Model for Walnut X-ray Image Denoising | 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 Application of the Improved Restormer Model for Walnut X-ray Image Denoising Haifeng Jiao, Hui Zhang, Long Wen, Shuai Ji This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6242376/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 May, 2025 Read the published version in Signal, Image and Video Processing → Version 1 posted 5 You are reading this latest preprint version Abstract X-ray imaging technology, with its exceptional capability for visualizing internal structures, plays a critical role in the nondestructive testing of agricultural products such as walnuts and almonds. To address the current challenges of high noise levels in walnut X-ray images and the difficulty in preserving critical image details after denoising, this study proposes an improved Restormer denoising model for clearer processing of walnut X-ray images. First, batch normalization layers were introduced into the Multi-Dconv Head Transposed Attention mechanism to enhance the model's understanding of image features. Second, a vertical Total Variation loss function was integrated to suppress high noise levels and extract clearer image features. Experimental results demonstrated that the improved Restormer model achieved a PSNR of 37.30, an SSIM of 0.9358, and an information entropy of 6.5600, representing improvements of 0.16 dB, 0.0002, and 0.0014, respectively, compared to the original Restormer model. In terms of PSNR, SSIM, information entropy, and visual quality, the proposed model outperformed six commonly used network models, including DnCNN and Uformer, delivering clearer images with richer details. Furthermore, on the local dataset, the model also exhibited excellent processing performance, generalization ability, and stability, making it a highly effective solution for walnut X-ray image denoising. The research results can offer a theoretical basis for the efficient image denoising method on walnut X-ray and provide valuable insights for denoising research in other imaging fields. Walnut X-ray Technology Image Denoising Restormer Attention Mechanism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 May, 2025 Read the published version in Signal, Image and Video Processing → Version 1 posted Editorial decision: Revision requested 24 Mar, 2025 Reviewers invited by journal 24 Mar, 2025 Editor assigned by journal 18 Mar, 2025 Submission checks completed at journal 18 Mar, 2025 First submitted to journal 17 Mar, 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-6242376","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432955119,"identity":"bb3f1493-baa4-4c9f-a82c-1bb53f42bec1","order_by":0,"name":"Haifeng Jiao","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Haifeng","middleName":"","lastName":"Jiao","suffix":""},{"id":432955120,"identity":"3a538e1d-6f77-4b77-85b1-2bcb896274e2","order_by":1,"name":"Hui Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAo0lEQVRIiWNgGAWjYBACgwOMjQ8YeEDMBOK1NBuQqoWBTQLCJFrLjeS2ah6Zwwz87DkGDD93EKHF/kZi280ZPIcZJHveGDD2niHKlsS2Gx+AWgxu5BgwM7YRqaUgAajFniQtDGBbJIjWcuZhs+QMnnQeiTPPCg72EqXlePrDz7w91nL87ckbH/wkRgsYMPZAIvMAsRqA4AcJakfBKBgFo2DkAQA2DDcHCxiSdAAAAABJRU5ErkJggg==","orcid":"","institution":"Xinjiang University","correspondingAuthor":true,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhang","suffix":""},{"id":432955121,"identity":"bd1d6f44-30ab-4c95-8578-d0446d6088d9","order_by":2,"name":"Long Wen","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Wen","suffix":""},{"id":432955122,"identity":"f977bc7a-af30-4af7-81d8-03e7e316fb5e","order_by":3,"name":"Shuai Ji","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Ji","suffix":""}],"badges":[],"createdAt":"2025-03-17 08:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6242376/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6242376/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11760-025-04274-6","type":"published","date":"2025-05-28T15:56:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83782705,"identity":"af063f19-9ba0-4a8c-b744-c1eb704fd38a","added_by":"auto","created_at":"2025-06-02 16:00:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":656073,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6242376/v1_covered_bed9bca8-001f-445d-a927-146d87499fa1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of the Improved Restormer Model for Walnut X-ray Image Denoising","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":"Walnut, X-ray Technology, Image Denoising, Restormer, Attention Mechanism","lastPublishedDoi":"10.21203/rs.3.rs-6242376/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6242376/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eX-ray imaging technology, with its exceptional capability for visualizing internal structures, plays a critical role in the nondestructive testing of agricultural products such as walnuts and almonds. To address the current challenges of high noise levels in walnut X-ray images and the difficulty in preserving critical image details after denoising, this study proposes an improved Restormer denoising model for clearer processing of walnut X-ray images. First, batch normalization layers were introduced into the Multi-Dconv Head Transposed Attention mechanism to enhance the model's understanding of image features. Second, a vertical Total Variation loss function was integrated to suppress high noise levels and extract clearer image features. Experimental results demonstrated that the improved Restormer model achieved a PSNR of 37.30, an SSIM of 0.9358, and an information entropy of 6.5600, representing improvements of 0.16 dB, 0.0002, and 0.0014, respectively, compared to the original Restormer model. 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