A Dual-Metric Framework for Image Filtering Performance Assessment via Eigenvalue Variance and Restoration Error Analysis

A Dual-Metric Framework for Image Filtering Performance Assessment via Eigenvalue Variance and Restoration Error Analysis | 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 Dual-Metric Framework for Image Filtering Performance Assessment via Eigenvalue Variance and Restoration Error Analysis Nabil Amein Ali, Mona M. Abd El-kareem This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7474908/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 Image filtering is a fundamental operation in computer vision and image processing, yet its evaluation remains challenging due to the conflicting requirements of noise suppression and structural preservation. Traditional performance measures often emphasize either statistical consistency or fidelity to reference images, but rarely provide a holistic perspective. In this paper, we introduce a dual-metric framework for image filtering performance assessment, combining eigenvalue variance with restoration error analysis. Eigenvalue variance serves as a structural descriptor, capturing variations in local image matrices that indicate texture retention and edge clarity. Restoration error complements this by quantifying fidelity to the original image, thereby reflecting the degree of noise suppression. By integrating these two complementary measures, the framework delivers a balanced evaluation that accounts for both perceptual quality and quantitative accuracy. Experimental studies across a range of filtering techniques including linear, nonlinear, and adaptive methods demonstrate that the dual-metric approach effectively differentiates filters and reveals inherent trade-offs. This comprehensive methodology offers a reliable foundation for filter selection and optimization in diverse image processing applications. Eigen Values Variance Noise Filter Restoration Error OpenCV 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-7474908","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506584191,"identity":"4c20f745-49f0-4aea-83ec-2e64742f3207","order_by":0,"name":"Nabil Amein Ali","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYFACHoYDDypsmO0PMx8A8iRkiNOScCaNneE4WwJICw9RWhgS2w7zM5znMYByCQBz9rMHDyS2MUszNvN8fnWjxoKHgf3w0Q34tFj25CUcSDjHZszMzLvNOucY0GE8aWk38GkxOJBjcCChjCeZDajFOIcNqEWCxwy/lvNvgFrYJOp7mHmeGef8I0bLDZAtbQbMEsw8zI9z24jS8g7olzMJzAbMbGbMuX0SPGwE/XI+9/CHDxX/mQ34Dz/+nPOtTo6f/fAxvFqQAZsEmCRWOQgwfyBF9SgYBaNgFIwcAAClIEfYsOOpOQAAAABJRU5ErkJggg==","orcid":"","institution":"Institute of Management information systems.","correspondingAuthor":true,"prefix":"","firstName":"Nabil","middleName":"Amein","lastName":"Ali","suffix":""},{"id":506584192,"identity":"8d24ebdb-a226-4da6-83b3-b3f8e1582977","order_by":1,"name":"Mona M. 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