Hybrid Image Denoising using Adaptive Conductance Function and Bi-dimensional Empirical Mode Decomposition

Hybrid Image Denoising using Adaptive Conductance Function and Bi-dimensional Empirical Mode Decomposition | 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 Hybrid Image Denoising using Adaptive Conductance Function and Bi-dimensional Empirical Mode Decomposition Sarah Benziane This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5707764/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 digital imaging, effective noise reduction is essential for maintaining image clarity, especially in fields such as medical imaging, remote sensing, and computer vision. Traditional denoising techniques often involve a trade-off between noise suppression and detail preservation, which can result in loss of essential image features. This paper presents a novel hybrid denoising approach that combines an Adaptive Conductance Function (ACF) with Bi-dimensional Empirical Mode Decomposition (BEMD). The proposed method leverages the adaptive properties of ACF to dynamically reduce noise based on local image gradients, preserving edges and fine details. The BEMD component decomposes the image across multiple frequency scales, allowing selective noise reduction on high-frequency components without affecting the underlying structural information. Experimental evaluations on synthetic and real-world datasets, including images with Gaussian and salt-and-pepper noise, demonstrate significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) compared to traditional methods. This hybrid method shows strong potential for applications requiring high-fidelity image denoising and detail preservation. Image Denoising Adaptive Conductance Function (ACF) Bi-dimensional Empirical Mode Decomposition (BEMD) Multiscale Denoising Edge Preservation Noise Suppression 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-5707764","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":413087339,"identity":"fe921d05-453a-468a-9e25-e561337ea914","order_by":0,"name":"Sarah Benziane","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIie3PsUrDQBjA8S8E6nKp60Eg8RG+cKCWvkyLkCxRO2aQeqWQSXCNb3GPkBBol5SsgXaoCJk6FDpWipciosJF3ATvP13u8uO7A9Dp/mAuBzABUrk05BKoA0CaA0dJMP1KLtk7YWoCH+RYNOQ/kpPF824EK+fiNJ/sRhENxHyRrbeAt8q3PATMTqBmPZ5N7aSg16K4ufISwB5XmdQHm0A+FNmE21YsSRqey51XVN6srM29JPciN6Z760ADLDcNQTWp/E4zZYAzI7YtTgdYhe0Eq7rTJ1B7ojDiPplR76naMC9BNXEffXNJYOViOX9Zkrux2y1Db72NWi7WZBw+fZylx+mt4Ptc/pu/dTqd7j/0Bl5oVVp+kKg8AAAAAElFTkSuQmCC","orcid":"","institution":"university of Sciences and Technology of Oran","correspondingAuthor":true,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Benziane","suffix":""}],"badges":[],"createdAt":"2024-12-24 18:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5707764/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5707764/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75973211,"identity":"7b74d8c2-4982-40a0-89cb-d8be9fd17920","added_by":"auto","created_at":"2025-02-11 06:05:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":616481,"visible":true,"origin":"","legend":"","description":"","filename":"HybridImageDenoisingusingAdaptiveConductanceV4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5707764/v1_covered_da350543-1fcd-4aec-ba08-22467f9c85d7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hybrid Image Denoising using Adaptive Conductance Function and Bi-dimensional Empirical Mode Decomposition","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":"Image Denoising, Adaptive Conductance Function (ACF), Bi-dimensional Empirical Mode Decomposition (BEMD), Multiscale Denoising, Edge Preservation, Noise Suppression","lastPublishedDoi":"10.21203/rs.3.rs-5707764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5707764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In digital imaging, effective noise reduction is essential for maintaining image clarity, especially in fields such as medical imaging, remote sensing, and computer vision. Traditional denoising techniques often involve a trade-off between noise suppression and detail preservation, which can result in loss of essential image features. This paper presents a novel hybrid denoising approach that combines an Adaptive Conductance Function (ACF) with Bi-dimensional Empirical Mode Decomposition (BEMD). The proposed method leverages the adaptive properties of ACF to dynamically reduce noise based on local image gradients, preserving edges and fine details. The BEMD component decomposes the image across multiple frequency scales, allowing selective noise reduction on high-frequency components without affecting the underlying structural information. Experimental evaluations on synthetic and real-world datasets, including images with Gaussian and salt-and-pepper noise, demonstrate significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) compared to traditional methods. This hybrid method shows strong potential for applications requiring high-fidelity image denoising and detail preservation.","manuscriptTitle":"Hybrid Image Denoising using Adaptive Conductance Function and Bi-dimensional Empirical Mode Decomposition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-11 05:41:32","doi":"10.21203/rs.3.rs-5707764/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":"d4d315eb-ecbb-468e-a713-350fe7b058d0","owner":[],"postedDate":"February 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-11T05:41:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-11 05:41:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5707764","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5707764","identity":"rs-5707764","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}