SRHPENet: Super-Resolution Network for human pose estimation

SRHPENet: Super-Resolution Network for human pose estimation | 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Article SRHPENet: Super-Resolution Network for human pose estimation Xuelian Zou, Xiaojun Bi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5335065/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 Human pose estimation is a basic task in the field of computer vision, so improving recognition accuracy is of great significance for advanced computer vision tasks. At present, there are many methods to solve the human pose estimation task of high-resolution images, and have achieved excellent results. However, current high-resolution human pose estimation methods still face two problems in solving low-resolution human pose estimation tasks. (1) Low-resolution images usually lose a lot of key information, such as texture information, joint point positions becoming blurred, etc., which will cause the recognition accuracy to decrease. (2) Existing super-resolution images generally have a fixed receptive field, and cannot extract features and fuse multi-scale information well for images with different magnifications. To fill the problem of super-resolution images, we proposed a method suitable for low-resolution images. Super-Resolution Human Pose Estimation Network (SRHPENet) for human pose estimation tasks. The network is composed of two parts. First, we designed SReNe super-resolution network. The network is composed of the MSE module we designed, which can effectively extract multi-scale information content and alleviate the problem of fixed receptive fields. Secondly, we input the high-resolution images obtained by the super-resolution network into the human pose estimation network. Additionally, during the training phase, we introduce real high-resolution images into the human pose estimation network in order to improve the accuracy of human pose estimation. Finally, through joint training of the two parts, we obtain low-resolution performance close to the performance of state-of the-art with high-resolution images as input. Human pose estimation Low-resolution images Super-resolution images Multi-scale feature extraction 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-5335065","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373727020,"identity":"98aafedf-610e-4867-8448-3d30d17f6c6e","order_by":0,"name":"Xuelian Zou","email":"","orcid":"","institution":"Harbin Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Xuelian","middleName":"","lastName":"Zou","suffix":""},{"id":373727021,"identity":"f7450931-441d-41a5-8ed0-61cbda3f69fd","order_by":1,"name":"Xiaojun Bi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACA2YGhgMMDGwMDOw9DMwQsQRitfCcIVYLnCWRQ6QWc3YewwM/KvgSt0u+Pfa5oOIwAz97jgHDzx24tVg2syUc7DnDlrhzdl7y7BlnDjNI9rwxYOw9g8dhh5kPHOBtY0vccDvHmJm37TCDwY0cA2bGNnxaGBsO/gVpuXkGqOXfYQZ7wlqYDxwG23KDB6ilAWiLBAEtIL8cljnDZrzhDNBhM46l80iceVZwsBePFnP+M8Yf31Qck91wHOiwghprOf725I0PfuLRAgXH4CweEHGAoAYGhhoi1IyCUTAKRsGIBQDsQVCjFyFcdwAAAABJRU5ErkJggg==","orcid":"","institution":"Minzu University of China","correspondingAuthor":true,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Bi","suffix":""}],"badges":[],"createdAt":"2024-10-26 02:08:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5335065/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5335065/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72935022,"identity":"808c9b93-bafb-4b44-9726-dc6e2fd99e3e","added_by":"auto","created_at":"2025-01-04 02:08:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2053032,"visible":true,"origin":"","legend":"","description":"","filename":"SignalImageandVideoProcessinglowhumanposeCopy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5335065/v1_covered_6488efc9-9be3-4b5b-95f5-fb1f60cd22cf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SRHPENet: Super-Resolution Network for human pose estimation","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":"Human pose estimation, Low-resolution images, Super-resolution images, Multi-scale feature extraction","lastPublishedDoi":"10.21203/rs.3.rs-5335065/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5335065/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Human pose estimation is a basic task in the field of computer vision, so improving recognition accuracy is of great significance for advanced computer vision tasks. 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The network is composed of the MSE module we designed, which can effectively extract multi-scale information content and alleviate the problem of fixed receptive fields. Secondly, we input the high-resolution images obtained by the super-resolution network into the human pose estimation network. Additionally, during the training phase, we introduce real high-resolution images into the human pose estimation network in order to improve the accuracy of human pose estimation. 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