A Multi-Scale Fusion Bogie Image Enhancement Algorithm Based on Collaborative Optimization of Image Features

A Multi-Scale Fusion Bogie Image Enhancement Algorithm Based on Collaborative Optimization of Image Features | 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 Multi-Scale Fusion Bogie Image Enhancement Algorithm Based on Collaborative Optimization of Image Features Xiaofeng Yao, Haoliang Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6218018/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 The structural integrity and performance stability of the bogie directly affect the safety of train travel. Currently, bogie safety monitoring based on computer vision is increasingly favored by users.But the problems of low image contrast, noise interference and blurring caused by uneven lighting and occlusion in the bogie detection environment need to be urgently addressed.This study proposes A multi-scale fusion bogie (MFB) image enhancement algorithm based on collaborative optimization of image features. The algorithm first uses the Retinex model for illumination estimation, combined with gamma correction to achieve brightness equalization, in order to improve the overall contrast of the image. Subsequently, the color distribution of the image is optimized through color gain weighting and white balance correction, enhancing the naturalness and fidelity of colors. Further use a sharpening method based on Laplacian operator to enhance edge features, and layer the image through multi-scale dehazing and denoising modules to reconstruct the image layer by layer. Finally, through a multi-scale weighted feature fusion strategy, images with color enhanced, edge sharpened, and noise reduced are weighted and fused to generate high-quality enhanced images. The experimental results show that compared with traditional image enhancement methods and deep learning based enhancement methods, our algorithm exhibits significant advantages in both subjective visual effects and objective indicators, especially in detail restoration and color fidelity. Image Enhancement Algorithm White Balance Correction Color Gain Weighting Image Edge Sharpening Multi-Scale Fusion 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. 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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-6218018","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432910308,"identity":"6db3899e-2526-49da-9c46-3cea8c07d671","order_by":0,"name":"Xiaofeng Yao","email":"","orcid":"","institution":"Dalian Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Yao","suffix":""},{"id":432910309,"identity":"c278ab44-cdfc-4030-a253-fcbbf8cdfa1d","order_by":1,"name":"Haoliang Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACNv7mA4f/VEjI8bM3HyBOC5/EscQHPGcsjCV7jiUQp0WOIcfYgLelItHgRo4BkQ5jOGMmIdkgkcBwI+fjjTcMdnK6DYS0MLeVSRjukMhj7Hm72XIOQ7Kx2QGCthzeJpF4RqKYmT13mzQPw4HEbYS1JJhJHGyTSGxjyHlGrJYUY8NGoJYejhw2IrUAA/kxwxkJYwmeY8aWcwyI8It8PzAqGSrq5OyPNz+88abCTo6gFhQgwUNk1CBrIVXHKBgFo2AUjAgAAES+Qh32SbIMAAAAAElFTkSuQmCC","orcid":"","institution":"Dalian Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Haoliang","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-03-13 08:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6218018/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6218018/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102598062,"identity":"a493cebc-fb3e-4e03-9310-ca0675961a56","added_by":"auto","created_at":"2026-02-13 12:27:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":569063,"visible":true,"origin":"","legend":"","description":"","filename":"Amultiscalefusionbogieimageenhancementalgorithmbasedoncollaborativeoptimizationofimagefeatures2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6218018/v1_covered_95bd9009-35bf-40f3-9232-ca5d41df66aa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multi-Scale Fusion Bogie Image Enhancement Algorithm Based on Collaborative Optimization of Image Features","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 Enhancement Algorithm, White Balance Correction, Color Gain Weighting, Image Edge Sharpening, Multi-Scale Fusion","lastPublishedDoi":"10.21203/rs.3.rs-6218018/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6218018/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe structural integrity and performance stability of the bogie directly affect the safety of train travel. Currently, bogie safety monitoring based on computer vision is increasingly favored by users.But the problems of low image contrast, noise interference and blurring caused by uneven lighting and occlusion in the bogie detection environment need to be urgently addressed.This study proposes A multi-scale fusion bogie (MFB) image enhancement algorithm based on collaborative optimization of image features. The algorithm first uses the Retinex model for illumination estimation, combined with gamma correction to achieve brightness equalization, in order to improve the overall contrast of the image. Subsequently, the color distribution of the image is optimized through color gain weighting and white balance correction, enhancing the naturalness and fidelity of colors. Further use a sharpening method based on Laplacian operator to enhance edge features, and layer the image through multi-scale dehazing and denoising modules to reconstruct the image layer by layer. Finally, through a multi-scale weighted feature fusion strategy, images with color enhanced, edge sharpened, and noise reduced are weighted and fused to generate high-quality enhanced images. 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