CasDyF-Net: Transforming Single-Image Dehazing through Federated and Adaptive CNNs

CasDyF-Net: Transforming Single-Image Dehazing through Federated and Adaptive CNNs | 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 CasDyF-Net: Transforming Single-Image Dehazing through Federated and Adaptive CNNs Rahat Naz, Krish Sen, Md Imam Mahdi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7650354/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 The Cascaded Dynamic Filter Network (CasDyF-Net) is introduced as a privacy-preserving framework for single-image dehazing, integrating Federated Learning (FL) with a lightweight, task-specific Convolutional Neural Network (CNN). Unlike centralized methods that require raw data transmission, CasDyF-Net ensures confidentiality by transmitting only model updates from edge devices. Its cascaded design incorporates multi-scale feature extraction, dynamic filters for adaptive haze removal, and progressive attention mechanisms to effectively address non-uniform haze patterns. A composite loss function, combining L1, Learned Perceptual Image Patch Similarity (LPIPS), and Structural Similarity Index Measure (SSIM), enhances perceptual fidelity beyond single-loss approaches. Evaluation on the RESIDE-6K dataset demonstrates performance with PSNR of 22.0, SSIM of 0.85, and LPIPS of 0.08, outperforming AOD-Net and SADnet while remaining competitive with FFA-Net and DR3DF-Net. By balancing privacy preservation, computational efficiency, and robustness to heterogeneous data, CasDyF-Net establishes a practical solution for real-world dehazing applications in domains such as intelligent transportation and remote sensing. Single-image dehazing Federated Learning CNN Edge computing non-IID data Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 27 Sep, 2025 Reviewers invited by journal 21 Sep, 2025 Editor assigned by journal 20 Sep, 2025 Submission checks completed at journal 20 Sep, 2025 First submitted to journal 18 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. We do this by developing innovative software and high quality services for the global research community. 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