Dynamic Nonlinear Networks for Adaptive Low-Light Image Enhancement

Dynamic Nonlinear Networks for Adaptive Low-Light Image Enhancement | 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 Dynamic Nonlinear Networks for Adaptive Low-Light Image Enhancement Minglong Xue, Kaiwen Chen, Senming Zhong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7910462/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Mar, 2026 Read the published version in The Visual Computer → Version 1 posted 13 You are reading this latest preprint version Abstract While diffusion-based methods have advanced low-light image enhancement, they face two critical limitations: the tendency to suppress complex high-frequency details by misinterpreting them as noise, and persistent artifacts such as insufficient denoising and color shifts. To address these challenges, we propose a novel framework, Dynamic Nonlinear Networks, inspired by the Kolmogorov-Arnold representation theorem. Our framework introduces a Dynamic Edge Feature Extractor (DEFE), which leverages learnable nonlinear convolutions and adaptive residual connections to reconstruct high-frequency features prior to the diffusion process, effectively preventing the diffusion process from erroneously suppressing fine textures. To further enforce structural fidelity, we introduce a dedicated edge-preserving loss. Furthermore, to tackle residual artifacts, we leverage a Residual Refinement Module that is conditioned on the original low-light input. This mechanism explicitly couples noise estimation with illumination degradation, enabling precise correction of noise and color deviations without sacrificing detail through uniform smoothing. Extensive experiments on benchmark datasets, including LOL and SICE, demonstrate that our method achieves state-of-the-art performance, particularly in preserving intricate details and ensuring color fidelity. The code is available at https://github.com/KKKKevin0401/DEFE Low-Light Image Enhancement Kolmogorov-Arnold Nonlinear Networks Diffusion-model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Mar, 2026 Read the published version in The Visual Computer → Version 1 posted Editorial decision: Revision requested 23 Nov, 2025 Reviews received at journal 23 Nov, 2025 Reviews received at journal 22 Nov, 2025 Reviews received at journal 20 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviewers invited by journal 04 Nov, 2025 Editor assigned by journal 22 Oct, 2025 Submission checks completed at journal 22 Oct, 2025 First submitted to journal 21 Oct, 2025 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-7910462","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540825047,"identity":"3c94e6a7-dc3c-402a-b681-5f14db31e15f","order_by":0,"name":"Minglong Xue","email":"","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Minglong","middleName":"","lastName":"Xue","suffix":""},{"id":540825048,"identity":"0c32780e-9ee5-467e-9d10-c1f857b82486","order_by":1,"name":"Kaiwen Chen","email":"","orcid":"","institution":"Chongqing University of 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