A GFP-GAN based method for improving clarity of license plate images

A GFP-GAN based method for improving clarity of license plate images | 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 GFP-GAN based method for improving clarity of license plate images Tianyun Huang, Jinxu Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4798789/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 License plate recognition is a very important component of intelligent transportation system, and its accuracy and speed affect the operation of the entire system. However, it is still difficult to achieve high accuracy in license plate positioning and character recognition for low-quality license plate images. To migrate the GFP-GAN model from blind face reconstruction to license plate image restoration, we made contributions to improve the clarity of license plate images in three aspects: (1). add motion blur present commonly on license plate images rather than facial images; (2). add the license plate recognition loss during training to increase the network's ability to recover correct characters; (3). align the region discriminator to the license plate position to optimize the region recovery effect. As in GFP-GAN, the multi-scale semantic information of low-quality license plate image is extracted by U-Net (extractor), and converted to coding that controls the image content (encoder), then the coding is input to a pre-trained StyleGAN2 network to generate the corresponding high quality license plate image (generator/decoder). The migrated model, datasets requirement and preparation, training process and algorithms are given in detail. As compared with existing methods, the experiments show that our method can significantly improve the accuracy on license plate recognition, with advantages of strong adaptability to various type of blurs, fine and smooth restoration quality, prominent emphasis on license plate area. License plate recognition Super-resolution reconstruction GAN U-Net StyleGAN2 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. 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-4798789","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":331560238,"identity":"5c5d1c7c-464a-4158-bd65-0f3cca0dc8d7","order_by":0,"name":"Tianyun Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACAziLvbHx4QcStAApnsPNxhKkaZFIbxPgIUaLuUTys4dfGP4wGNx82MYgwWAnp9tAQIvljDRzYxmgLQa3E9seFDAkG5sdIOSwGwlm0hIQLe0GEgwHErcR1pL+DaLl5sE2CR7itOSYSX4AabnBSKyWM2/KpBkMjHkkzyQCA9mAGL8cT98m+aNCTo7v+PGHDz9U2MkR1AICzDwGDDwKYJUGBJTCAOMPICHfQKTqUTAKRsEoGHkAADGhPq3f7mbFAAAAAElFTkSuQmCC","orcid":"","institution":"Southwest Minzu University","correspondingAuthor":true,"prefix":"","firstName":"Tianyun","middleName":"","lastName":"Huang","suffix":""},{"id":331560239,"identity":"f3fd08ee-51d1-4769-aa9f-69a9c9e3e6c0","order_by":1,"name":"Jinxu Wei","email":"","orcid":"","institution":"Southwest Minzu University","correspondingAuthor":false,"prefix":"","firstName":"Jinxu","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2024-07-25 04:01:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4798789/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4798789/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65661791,"identity":"5e4f1761-2c54-403f-acc5-f710b1594ecf","added_by":"auto","created_at":"2024-10-01 04:53:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2594498,"visible":true,"origin":"","legend":"","description":"","filename":"20240721En.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4798789/v1_covered_fd2989f1-d3a7-4880-9a40-8342c51ba29e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A GFP-GAN based method for improving clarity of license plate images","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"License plate recognition, Super-resolution reconstruction, GAN, U-Net, StyleGAN2","lastPublishedDoi":"10.21203/rs.3.rs-4798789/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4798789/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLicense plate recognition is a very important component of intelligent transportation system, and its accuracy and speed affect the operation of the entire system. However, it is still difficult to achieve high accuracy in license plate positioning and character recognition for low-quality license plate images. To migrate the GFP-GAN model from blind face reconstruction to license plate image restoration, we made contributions to improve the clarity of license plate images in three aspects: (1). add motion blur present commonly on license plate images rather than facial images; (2). add the license plate recognition loss during training to increase the network's ability to recover correct characters; (3). align the region discriminator to the license plate position to optimize the region recovery effect. As in GFP-GAN, the multi-scale semantic information of low-quality license plate image is extracted by U-Net (extractor), and converted to coding that controls the image content (encoder), then the coding is input to a pre-trained StyleGAN2 network to generate the corresponding high quality license plate image (generator/decoder). The migrated model, datasets requirement and preparation, training process and algorithms are given in detail. As compared with existing methods, the experiments show that our method can significantly improve the accuracy on license plate recognition, with advantages of strong adaptability to various type of blurs, fine and smooth restoration quality, prominent emphasis on license plate area.\u003c/p\u003e","manuscriptTitle":"A GFP-GAN based method for improving clarity of license plate images","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-16 11:04:41","doi":"10.21203/rs.3.rs-4798789/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"ca73586d-bad8-49f3-adcd-a6d4adcb51b9","owner":[],"postedDate":"August 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-01T04:53:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-16 11:04:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4798789","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4798789","identity":"rs-4798789","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}