Research on the Barcode Deblurring Algorithm Based on a GAN

Research on the Barcode Deblurring Algorithm Based on a GAN | 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 Article Research on the Barcode Deblurring Algorithm Based on a GAN ChaoChao Li, LiKun Lu, QingTao Zeng, LiQin Yu, AnPing Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5958533/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 With the development of Internet of Things (IoT) technology, barcode automatic recognition systems play a crucial role. Traditional methods often perform poorly when processing blurred barcodes, which affects recognition and application performance. This paper proposes a barcode deblurring algorithm based on generative adversarial networks (GANs), aimed at overcoming the problem of insufficient barcode clarity in traditional image processing. First, the SE attention mechanism is combined with the aggregation residual block ResNeXt to form SE-ResNeXt, replacing the residual block ResNet, which accelerates the model’s convergence speed and enhances the stability of the training process. Second, the channel prior convolutional attention (CPCA) mechanism is introduced to improve the network's feature extraction ability and detection performance. The experimental results show that the proposed model achieves a peak signal-to-noise ratio (PSNR) of 30.48 dB, an improvement of 4.87 dB over the baseline network, and a structural similarity index (SSIM) of 0.9383, an improvement of 7.72%. The subjective visual deblurring effect is also promising, with restored barcode images showing clear edge contours and noticeable detail recovery. Physical sciences/Mathematics and computing Physical sciences/Mathematics and computing/Computer science Deblurring Generative adversarial networks Attention mechanism Barcode 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. 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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-5958533","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":412140480,"identity":"23ad5979-f50e-45ee-9e25-8d1bc20d82de","order_by":0,"name":"ChaoChao Li","email":"","orcid":"","institution":"Beijing Institute of Graphic Communication","correspondingAuthor":false,"prefix":"","firstName":"ChaoChao","middleName":"","lastName":"Li","suffix":""},{"id":412140481,"identity":"19b388dc-1c76-40d1-ab8e-488a313b4b73","order_by":1,"name":"LiKun Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACPh7mBoaPDRJgjgRRWth4GBsYZ5KshZm3gYEkLQfbpG13WNj1NzAfvM3DYJdHWAtvY5t07hmJ5BkH2JKteRiSiwlr4WcEammTSDZg4DGT5mE4kNhAlBZLsBb+b0RqATmMsU3CDmgLG5FaeA42W/aekUiQOMxmbDnHIJmwFn6e5IM3fu6os+dvb354402FHWEtMJDYwAyiDIhVDwT2JKgdBaNgFIyCkQYAw0Qwtv8X8ZkAAAAASUVORK5CYII=","orcid":"","institution":"Beijing Institute of Graphic Communication","correspondingAuthor":true,"prefix":"","firstName":"LiKun","middleName":"","lastName":"Lu","suffix":""},{"id":412140482,"identity":"030c3204-b7e4-4f9f-854c-3872c89cbfff","order_by":2,"name":"QingTao Zeng","email":"","orcid":"","institution":"Beijing Institute of Graphic Communication","correspondingAuthor":false,"prefix":"","firstName":"QingTao","middleName":"","lastName":"Zeng","suffix":""},{"id":412140483,"identity":"fa548b6e-578c-4d3a-899c-2a1480c24983","order_by":3,"name":"LiQin Yu","email":"","orcid":"","institution":"Beijing Institute of Graphic Communication","correspondingAuthor":false,"prefix":"","firstName":"LiQin","middleName":"","lastName":"Yu","suffix":""},{"id":412140484,"identity":"0f1048a6-dad0-4c6b-8190-59fd98d0db53","order_by":4,"name":"AnPing Xu","email":"","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"AnPing","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-02-04 13:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5958533/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5958533/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81927734,"identity":"40e29761-61d8-4ba9-9e7f-7a90ce054c24","added_by":"auto","created_at":"2025-05-05 04:16:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1587898,"visible":true,"origin":"","legend":"","description":"","filename":"file.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5958533/v1_covered_d016b7aa-acbf-48c1-ae5d-d5090d3cb216.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the Barcode Deblurring Algorithm Based on a GAN","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":"Deblurring, Generative adversarial networks, Attention mechanism, Barcode","lastPublishedDoi":"10.21203/rs.3.rs-5958533/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5958533/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the development of Internet of Things (IoT) technology, barcode automatic recognition systems play a crucial role. Traditional methods often perform poorly when processing blurred barcodes, which affects recognition and application performance. This paper proposes a barcode deblurring algorithm based on generative adversarial networks (GANs), aimed at overcoming the problem of insufficient barcode clarity in traditional image processing. First, the SE attention mechanism is combined with the aggregation residual block ResNeXt to form SE-ResNeXt, replacing the residual block ResNet, which accelerates the model\u0026rsquo;s convergence speed and enhances the stability of the training process. Second, the channel prior convolutional attention (CPCA) mechanism is introduced to improve the network's feature extraction ability and detection performance. The experimental results show that the proposed model achieves a peak signal-to-noise ratio (PSNR) of 30.48 dB, an improvement of 4.87 dB over the baseline network, and a structural similarity index (SSIM) of 0.9383, an improvement of 7.72%. 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