Infrared and Visible Image Alignment Method Based on Gradient Distribution Properties and Scale Invariant Features in Electric Power Scenes

Infrared and Visible Image Alignment Method Based on Gradient Distribution Properties and Scale Invariant Features in Electric Power Scenes | 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 Infrared and Visible Image Alignment Method Based on Gradient Distribution Properties and Scale Invariant Features in Electric Power Scenes Lin Zhu, Yuxing Mao, Chunxu Chen, Lanjia Ning This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5331545/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 In the grid intelligent inspection system, automatic registration of infrared and visible light images in power scenes is a crucial research technology. Since there are obvious differences in key attributes between visible and infrared images, direct alignment is often difficult to achieve the expected results. To overcome the high difficulty of aligning infrared and visible light images, an image alignment method is proposed in this paper. First, we use the Sobel operator to extract the edge information of the image pair. Second, the feature points in the edges are recognized by a curvature scale space (CSS)corner detector. Third, the Histogram of Orientation Gradients (HOG) is extracted as the gradient distribution characteristics of the feature points, which are normalized with the Scale Invariant Feature Transform (SIFT) algorithm to form feature descriptors. Finally, initial matching and accurate matching are achieved by the improved fast approximate nearest neighbor matching method and adaptive thresholding, respectively. Experiments show that this method can robustly match the feature points of image pairs under rotation, scale, and viewpoint differences, and achieves excellent matching results. image alignment infrared and visible image electricity inspection gradient direction characterization 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-5331545","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":370356293,"identity":"e53db03b-b8c7-430c-a811-5b4eba458ebd","order_by":0,"name":"Lin Zhu","email":"","orcid":"","institution":"Chongqing University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Zhu","suffix":""},{"id":370356294,"identity":"45c07693-bf90-4130-820c-474105fb1e9e","order_by":1,"name":"Yuxing Mao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYFAD9sbGhx9I08JzuNlYgjQtEultAjzEKNSdkWPA+HWPTeJ2yYdtDBIMdnK6DQS0mN1IS2CWeZaWuHN2YtuDAoZkY7MDBLUkH2CWOHA4ccPtxHYDCYYDidsIa0lsgGi5ebBNgoc4LckHGD+AtNxgJFbLmWcJzAwH0ox39iQCA9mAGL8cB4bYjwM2stvZjz98+KHCTo6gFgaBBPbfoOgwAPMMCCkHAf4DDIw/iFU8CkbBKBgFIxMAAIf+R3lknBSKAAAAAElFTkSuQmCC","orcid":"","institution":"Chongqing University","correspondingAuthor":true,"prefix":"","firstName":"Yuxing","middleName":"","lastName":"Mao","suffix":""},{"id":370356295,"identity":"500fbace-157f-4c7a-8347-00d8016dd305","order_by":2,"name":"Chunxu Chen","email":"","orcid":"","institution":"Chongqing University","correspondingAuthor":false,"prefix":"","firstName":"Chunxu","middleName":"","lastName":"Chen","suffix":""},{"id":370356296,"identity":"68430a18-8aa4-465d-868d-092682df3896","order_by":3,"name":"Lanjia Ning","email":"","orcid":"","institution":"Chongqing University","correspondingAuthor":false,"prefix":"","firstName":"Lanjia","middleName":"","lastName":"Ning","suffix":""}],"badges":[],"createdAt":"2024-10-25 10:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5331545/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5331545/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69674229,"identity":"5dd2fad5-97d5-4650-84f4-92f73bdd1329","added_by":"auto","created_at":"2024-11-23 03:16:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8952788,"visible":true,"origin":"","legend":"","description":"","filename":"snarticletemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5331545/v1_covered_387b9f2a-3d75-40d2-853c-49076c2100bf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Infrared and Visible Image Alignment Method Based on Gradient Distribution Properties and Scale Invariant Features in Electric Power Scenes","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":"image alignment, infrared and visible image, electricity inspection, gradient direction characterization","lastPublishedDoi":"10.21203/rs.3.rs-5331545/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5331545/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the grid intelligent inspection system, automatic registration of infrared and visible light images in power scenes is a crucial research technology. 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