UnionFusion: Improving Misaligned Multi-modality Image Registration And Fusion Via Feature Enhancement

UnionFusion: Improving Misaligned Multi-modality Image Registration And Fusion Via Feature 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 UnionFusion: Improving Misaligned Multi-modality Image Registration And Fusion Via Feature Enhancement Yunde Zhang, Jun Kong, Ming Lu, Xuefeng Tao, Min Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6342395/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Feb, 2026 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted 14 You are reading this latest preprint version Abstract Image registration and fusion aim to align multi-modality images, generating fused image with richer information and higher quality.Existing fusion methods correct spatial misalignment through geometric transformation, semantic guidance, and cross-modality complementarity.However, these methods adopt fixed receptive fields, overlooking local fine-grained features, which leads to structural distortions and edge artifacts.To address these issues, this paper proposes an improved approach for misaligned multi-modality image registration and fusion via feature enhancement, called UnionFusion.Firstly, to achieve the elastic receptive field in registration and fusion tasks, we develop Separable Deformable Convolution (SDConv), which utilizes group-specific learnable sampling offsets to enhance feature representation.Secondly, to address image spatial misalignment, we design Multi-scale Deformable Adaptive Registration module (MDAR) toestimate the deformation field between the source and target images, correcting geometric distortions.Thirdly, to enhance fine-grained features, we design differential fusion mechanism, Shallow Deformable Fusion (SDF) to extract shallow features and Deep Progressive Fusion (DPF) to capture deep hierarchical features.Finally, to enhance the complementarity of the joint image registration and fusion, we employ symmetric network and symmetric loss.Extensive experimental analysis demonstrates the superiority of the proposed method in three multi-modality image fusion datasets. Image registration and fusion Multi-modality Deformable convolution Fine-grained features Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Feb, 2026 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted Editorial decision: Revision requested 21 Jul, 2025 Reviews received at journal 04 Jul, 2025 Reviews received at journal 03 Jul, 2025 Reviews received at journal 16 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers agreed at journal 14 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviewers agreed at journal 08 Jun, 2025 Reviewers agreed at journal 14 Apr, 2025 Reviewers invited by journal 14 Apr, 2025 Editor assigned by journal 09 Apr, 2025 Submission checks completed at journal 07 Apr, 2025 First submitted to journal 31 Mar, 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-6342395","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447301966,"identity":"080b7b7e-ea3e-44dc-8dfd-873bbfb8ff65","order_by":0,"name":"Yunde Zhang","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Yunde","middleName":"","lastName":"Zhang","suffix":""},{"id":447301967,"identity":"fbc17398-529c-416f-878e-1566861123d9","order_by":1,"name":"Jun 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