Bidirectional Aware Vision Mamba for Lightweight Single Image Super-Resolution

Bidirectional Aware Vision Mamba for Lightweight Single Image Super-Resolution | 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 Bidirectional Aware Vision Mamba for Lightweight Single Image Super-Resolution Xin Wu, Junfeng Yang, Dongyang Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7927659/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 7 You are reading this latest preprint version Abstract Single image super-resolution (SISR) aims to recover high-resolution images from their low-resolution counterparts. Despite significant progress driven by deep learning, existing CNN- and Transformer-based methods struggle to balance reconstruction fidelity with computational efficiency. CNNs suffer from limited receptive fields, while Transformers incur prohibitive computational costs due to their quadratic attention complexity. Recent State Space Models (SSMs) have emerged as promising alternatives, offering linear complexity and strong long range modeling capabilities. However, standard Mamba processes images via unidirectional 2D scanning, inadequately capturing rich global visual context. To address this limitation, we propose the Bidirectional Aware Mamba Network (BAMN), a novel lightweight U-shaped architecture that leverages Bidirectional Scan Mamba Blocks (BSMB) to comprehensively model contextual information from both forward and backward directions. BAMN further incorporates a Global Context Fusion Block (GCFB) within skip connections to effectively aggregate multi-scale features across encoder and decoder stages, enabling high-fidelity reconstruction of both local textures and global structures. Extensive experiments on standard benchmarks demonstrate that BAMN outperforms state-of-the-art methods in both quantitative metrics and visual quality, while maintaining a compact model size and low computational overhead. Super-Resolution State Space Model Deep Learning Fast Fourier Transform Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 09 May, 2026 Reviews received at journal 07 May, 2026 Reviewers agreed at journal 29 Oct, 2025 Reviewers invited by journal 27 Oct, 2025 Editor assigned by journal 24 Oct, 2025 Submission checks completed at journal 24 Oct, 2025 First submitted to journal 22 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. 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-7927659","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541213674,"identity":"b61f710d-288c-4ce5-b395-4231f9f4caa7","order_by":0,"name":"Xin Wu","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wu","suffix":""},{"id":541213675,"identity":"18d8f17c-4ecf-466f-a83a-816f39bf0756","order_by":1,"name":"Junfeng Yang","email":"","orcid":"","institution":"University of Electronic Science and Technology 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