USF-Net: U-shaped Mamba Dehazing Network Enhanced by Wavelet Features and Spatial Position Fusion

USF-Net: U-shaped Mamba Dehazing Network Enhanced by Wavelet Features and Spatial Position Fusion | 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 USF-Net: U-shaped Mamba Dehazing Network Enhanced by Wavelet Features and Spatial Position Fusion Longxiang Fang, Wang Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8527226/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 In this paper, we employ wavelet transform analysis to reveal the wavelet degradation prior to haze. Through this prior, we demonstrate that haze- related information primarily resides in low-frequency components, while its effect on high-frequency components manifests mainly as edge blurring and tex- ture detail attenuation. Leveraging this insight, we propose a novel dehazing framework, USF-Net, which decouples the ill-posed image dehazing problem into two subtasks: position-guided channel selective enhancement and spatial- aware multi-scale feature extraction. Specifically, we integrate Mamba blocks with spatially-informed channel-weighted attention modules, achieving global structure reconstruction with linear complexity while effectively fusing spatial and channel information. Additionally, we incorporate position-aware multi- receptive-field attention modules to efficiently aggregate multi-scale spatial features, enhancing the network’s perception of spatial structures under varying haze concentrations. This design significantly improves both local detail fidelity and global semantic understanding. Extensive experimental results demonstrate that our method achieves state-of-the-art performance on both synthetic datasets and real-world hazy images. Artificial Intelligence and Machine Learning Artificial Intelligence and Machine Learning Image Dehazing Wavelet Degradation Prior Spatial Information Multi-scale Feature Fusion Full Text Additional Declarations The authors declare no competing interests. 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. 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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-8527226","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":569991216,"identity":"94d50484-a61f-4105-ba3c-4f2077fa62e2","order_by":0,"name":"Longxiang 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Through this prior, we demonstrate that haze- related information primarily resides in low-frequency components, while its effect on high-frequency components manifests mainly as edge blurring and tex- ture detail attenuation. Leveraging this insight, we propose a novel dehazing framework, USF-Net, which decouples the ill-posed image dehazing problem into two subtasks: position-guided channel selective enhancement and spatial- aware multi-scale feature extraction. Specifically, we integrate Mamba blocks with spatially-informed channel-weighted attention modules, achieving global structure reconstruction with linear complexity while effectively fusing spatial and channel information. Additionally, we incorporate position-aware multi- receptive-field attention modules to efficiently aggregate multi-scale spatial features, enhancing the network\u0026rsquo;s perception of spatial structures under varying haze concentrations. 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