A progressive multimodal image fusion algorithm

A progressive multimodal image fusion algorithm | 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Article A progressive multimodal image fusion algorithm Han Wu, Zihao Ying, Fangjun Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8550635/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 Image fusion encounters significant challenges when dealing with diverse image types, particularly due to modality discrepancies and imbalances in information content. To address these issues, we introduce a novel three-branch progressive gated multi-scene image fusion framework (TBPGFusion) designed to efficiently integrate images from heterogeneous modalities. The proposed model consists of three primary components: a three-branch progressive feature extraction module, a feature fusion module, and a secondary gated fusion module. Within the feature extraction module, a downsampling progressive fusion branch is employed. The Cross-modality Differential Aware Fusion (CMDAF) unit is utilized to compensate for differential information by leveraging same-layer features extracted from the three downsampled branches. Subsequently, the feature fusion module employs an improved Gated Fusion Module (IGFM) in conjunction with an L2-norm strategy to perform an initial fusion of the three-branch feature representations. The secondary gated fusion module further refines the fusion by integrating the differential information compensation features with the preliminarily fused features, again utilizing the IGFM. Through progressive upsampling, the number of channels is reduced to generate the final fused image. Experimental results indicate that the TBPGFusion framework effectively mitigates challenges associated with modality differences and information richness imbalance, demonstrating superior performance across multiple evaluation metrics. Notably, the model excels in preserving structural similarity and fine details, thereby substantially improving the overall quality of the fused images. image fusion progressive fusion multimodal cross-modality aware fusion gated 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. 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-8550635","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571438443,"identity":"377d7099-66e4-4e70-8505-d5270e020d1a","order_by":0,"name":"Han 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To address these issues, we introduce a novel three-branch progressive gated multi-scene image fusion framework (TBPGFusion) designed to efficiently integrate images from heterogeneous modalities. The proposed model consists of three primary components: a three-branch progressive feature extraction module, a feature fusion module, and a secondary gated fusion module. Within the feature extraction module, a downsampling progressive fusion branch is employed. The Cross-modality Differential Aware Fusion (CMDAF) unit is utilized to compensate for differential information by leveraging same-layer features extracted from the three downsampled branches. Subsequently, the feature fusion module employs an improved Gated Fusion Module (IGFM) in conjunction with an L2-norm strategy to perform an initial fusion of the three-branch feature representations. The secondary gated fusion module further refines the fusion by integrating the differential information compensation features with the preliminarily fused features, again utilizing the IGFM. Through progressive upsampling, the number of channels is reduced to generate the final fused image. Experimental results indicate that the TBPGFusion framework effectively mitigates challenges associated with modality differences and information richness imbalance, demonstrating superior performance across multiple evaluation metrics. Notably, the model excels in preserving structural similarity and fine details, thereby substantially improving the overall quality of the fused images.\u003c/p\u003e","manuscriptTitle":"A progressive multimodal image fusion algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-09 07:09:31","doi":"10.21203/rs.3.rs-8550635/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"31086f37-da91-4031-b081-821a940d51ef","owner":[],"postedDate":"January 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-09T07:09:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-09 07:09:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8550635","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8550635","identity":"rs-8550635","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}