A new optimized framework based on fractional-order gradients for enhancement of color digital images

A new optimized framework based on fractional-order gradients for enhancement of color digital images | 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 new optimized framework based on fractional-order gradients for enhancement of color digital images J. D. Pereyra-Guzmán, J. E. Solís-Pérez, J. F. Gómez-Aguilar, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5450995/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract This work proposes an optimized framework to enhance brightness while preserving textures and details in color digital images. The proposed method uses the conformable Gaussian and fractional Caputo–Fabrizio gradient to process the red (r), green (g), and blue (b) channels separately. The optimal models were reached via the Simulated Annealing algorithm to find the best parameters with an acceptable performance. The numerical simulations show that the parameter ξ in the fractional gradients works as a low-pass filter, whereas the conformable Gaussian gradient works as a low and high-pass filter. The proposed processing procedure is compared with other works in the literature, showing less image degradation, an acceptable structural similarity index, high contrast, and better perceptual quality. Fractional gradients Conformable Gaussian gradient conformable Gaussian kernel Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Nov, 2024 Reviewers invited by journal 23 Nov, 2024 Editor assigned by journal 14 Nov, 2024 Submission checks completed at journal 14 Nov, 2024 First submitted to journal 14 Nov, 2024 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-5450995","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":381820487,"identity":"30d90d95-fea2-407d-9e8e-ad81737595e3","order_by":0,"name":"J. D. 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