Enhancing Qualitative Microwave Imaging Indicators by Autoencoder Based Deep Learning Algorithm

Enhancing Qualitative Microwave Imaging Indicators by Autoencoder Based Deep Learning Algorithm | 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 Enhancing Qualitative Microwave Imaging Indicators by Autoencoder Based Deep Learning Algorithm İbrahim Halil BAYAT, Semih DOĞU, Mehmet Nuri AKINCI, Lorenzo CROCCO, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4056506/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 Enhancing microwave inverse imaging methods has garnered significant attention in the field of deep learning and microwave imaging, owing to their immense potential in tackling non-linear problems. Microwave imaging techniques commonly encounter the adverse effects of noise in measurements, leading to suboptimal results. In this research, we propose a novel deep neural network-based model that effectively mitigates the noise effects on qualitative microwave indicators, resulting in high-quality reconstructions. We consider various configurations of dielectric objects in free space to address the microwave imaging problem. The scattered field is computed using the two-dimensional method of moments, employing an antenna array comprising 36 infinitely long line sources. Subsequently, the linear sampling method is utilized to generate multi-frequency qualitative indicators, which are then employed as input to a deep learning network along with corresponding binary ground-truth images. The proposed deep learning model comprises 11 layers, including convolutional and deconvolutional layers, without any dropout or pooling layers to prevent information loss. The results of the network are compared with Jaccard index to see how similar they are. The proposed method achieves higher accuracy and Jaccard score, demonstrating the efficacy of the proposed deep learning approach. Notably, the simplicity of the model construction contributes to its appeal, as it operates in conjunction with the results obtained from the linear sampling method. The presented results underscore the remarkable capabilities of the proposed deep learning method in enhancing microwave inverse imaging. Deep Neural Networks Microwave Imaging Qualitative Imaging 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-4056506","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278297245,"identity":"894a7e30-c65b-46cf-a322-8a0709738b03","order_by":0,"name":"İbrahim Halil BAYAT","email":"","orcid":"","institution":"Istanbul Technical University","correspondingAuthor":false,"prefix":"","firstName":"İbrahim","middleName":"Halil","lastName":"BAYAT","suffix":""},{"id":278297246,"identity":"a8b71b4f-2387-43c0-9948-af03865501b9","order_by":1,"name":"Semih DOĞU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYDCCA0CcUGHDA+bwEK3lwZk0Hh6oFgmitDA+bDnMQLwWvuPt1yQSG87L2EskMD5428ZQZ95AQIvkmTNlEok7bvPwSCQwG85tY5CQOUBAi8GNnDSJxDNgLWzSvEAtBF0G0dJ2DqSF/TeRWtKPAbUcANvCTJQWoF+YLRLOJPPwnHnYLDnnnITkDEJagCH28OaPCjt79vbkgx/elNnwExExPAZQBmMDA1ExycDA/oAYVaNgFIyCUTCSAQD13Ttn30wBggAAAABJRU5ErkJggg==","orcid":"","institution":"Istanbul Technical University","correspondingAuthor":true,"prefix":"","firstName":"Semih","middleName":"","lastName":"DOĞU","suffix":""},{"id":278297247,"identity":"970a3e8b-7091-493b-a0f8-eecbb316b395","order_by":2,"name":"Mehmet Nuri AKINCI","email":"","orcid":"","institution":"Istanbul Technical University","correspondingAuthor":false,"prefix":"","firstName":"Mehmet","middleName":"Nuri","lastName":"AKINCI","suffix":""},{"id":278297248,"identity":"b3186fe9-e9d7-459b-8892-4cf59e936118","order_by":3,"name":"Lorenzo CROCCO","email":"","orcid":"","institution":"National Research Council","correspondingAuthor":false,"prefix":"","firstName":"Lorenzo","middleName":"","lastName":"CROCCO","suffix":""},{"id":278297249,"identity":"fd0165d7-9c33-42ea-8f19-a6d192cee2ff","order_by":4,"name":"İbrahim AKDUMAN","email":"","orcid":"","institution":"Istanbul Technical University","correspondingAuthor":false,"prefix":"","firstName":"İbrahim","middleName":"","lastName":"AKDUMAN","suffix":""}],"badges":[],"createdAt":"2024-03-09 13:46:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4056506/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4056506/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54859977,"identity":"6afa22a7-c506-4a95-a55c-f5005b90bc5e","added_by":"auto","created_at":"2024-04-17 19:23:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4731482,"visible":true,"origin":"","legend":"","description":"","filename":"enhancingqualitativeindicatersbyusingdeeplearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4056506/v1_covered_5777c311-8b04-4667-9c00-fe451f36b069.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Qualitative Microwave Imaging Indicators by Autoencoder Based Deep Learning Algorithm","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":"Deep Neural Networks, Microwave Imaging, Qualitative Imaging","lastPublishedDoi":"10.21203/rs.3.rs-4056506/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4056506/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Enhancing microwave inverse imaging methods has garnered significant attention in the field of deep learning and microwave imaging, owing to their immense potential in tackling non-linear problems. Microwave imaging techniques commonly encounter the adverse effects of noise in measurements, leading to suboptimal results. In this research, we propose a novel deep neural network-based model that effectively mitigates the noise effects on qualitative microwave indicators, resulting in high-quality reconstructions. We consider various configurations of dielectric objects in free space to address the microwave imaging problem. The scattered field is computed using the two-dimensional method of moments, employing an antenna array comprising 36 infinitely long line sources. Subsequently, the linear sampling method is utilized to generate multi-frequency qualitative indicators, which are then employed as input to a deep learning network along with corresponding binary ground-truth images. The proposed deep learning model comprises 11 layers, including convolutional and deconvolutional layers, without any dropout or pooling layers to prevent information loss. 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