Lightweight Image Dehazing via Physics-Guided Neural Networks

Lightweight Image Dehazing via Physics-Guided Neural Networks | 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 Lightweight Image Dehazing via Physics-Guided Neural Networks GUANGYUAN LIU, Min-Po Jung This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7717614/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Images taken in smoggy weather have problems such as dim colors, low contrast, and target details being obscured by fog. Moreover, the degradation of image quality can reduce the accuracy and robustness of subsequent computer vision tasks. Therefore, the restoration of clear images through image enhancement or physical models is an important technology in the visual perception systems. In this paper, a lightweight image dehazing method combined with physical models is proposed, which aims to achieve a lighter and more effective image dehazing process. Specifically, this paper introduces a dehazing network structure based on a Physics-Guided Neural Network (PGNN). The network structure explicitly restores key intermediate processes in the Atmospheric Scattering Model (ASM) and enhances the physical consistency of the network learning process, thereby achieving the purpose of effective fog removal. In addition, this paper proposes a Physics-Guided Loss (PGL) function based on the physical model, which can guide the network toward the real physical fog removal process. In order to meet the deployment needs, especially the real-time operation requirements on embedded devices or edge platforms, this paper implements a preliminary lightweight design for the network structure and builds a complete experimental evaluation framework to validate the efficacy of the proposed method. Through quantitative analysis and visual comparison, the experimental results demonstrate the significant advantages of PGNN in improving image quality and optimizing deployment efficiency. The ablation study further supports the contribution of PGNN to model performance. image dehazing physics-guided neural network (PGNN) lightweight neural network (Lightweight NN) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Nov, 2025 Reviews received at journal 11 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviews received at journal 26 Oct, 2025 Reviewers agreed at journal 25 Oct, 2025 Reviewers agreed at journal 25 Oct, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviewers invited by journal 24 Oct, 2025 Editor assigned by journal 27 Sep, 2025 Submission checks completed at journal 27 Sep, 2025 First submitted to journal 26 Sep, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7717614","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":535075112,"identity":"d005eb34-0197-4c89-ab51-e00180062c7a","order_by":0,"name":"GUANGYUAN LIU","email":"","orcid":"","institution":"Graduate School Youngsan University","correspondingAuthor":false,"prefix":"","firstName":"GUANGYUAN","middleName":"","lastName":"LIU","suffix":""},{"id":535075113,"identity":"9664f55a-45f4-4306-b948-65f100d7cd6a","order_by":1,"name":"Min-Po 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Moreover, the degradation of image quality can reduce the accuracy and robustness of subsequent computer vision tasks. Therefore, the restoration of clear images through image enhancement or physical models is an important technology in the visual perception systems. In this paper, a lightweight image dehazing method combined with physical models is proposed, which aims to achieve a lighter and more effective image dehazing process. Specifically, this paper introduces a dehazing network structure based on a Physics-Guided Neural Network (PGNN). The network structure explicitly restores key intermediate processes in the Atmospheric Scattering Model (ASM) and enhances the physical consistency of the network learning process, thereby achieving the purpose of effective fog removal. In addition, this paper proposes a Physics-Guided Loss (PGL) function based on the physical model, which can guide the network toward the real physical fog removal process. In order to meet the deployment needs, especially the real-time operation requirements on embedded devices or edge platforms, this paper implements a preliminary lightweight design for the network structure and builds a complete experimental evaluation framework to validate the efficacy of the proposed method. Through quantitative analysis and visual comparison, the experimental results demonstrate the significant advantages of PGNN in improving image quality and optimizing deployment efficiency. 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