{"paper_id":"41d03d3a-e140-4fc2-89f9-fcee24635902","body_text":"A foundation model for multi-task cross-distribution restoration of fluorescence microscopy image | 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 Article A foundation model for multi-task cross-distribution restoration of fluorescence microscopy image Shenghua Cheng, Qiqi Lu, Xiuli Liu, Qianjin Feng, Shaoqun zeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7431973/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Mar, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Deep learning-based methods have demonstrated remarkable abilities in restoring high-quality fluorescence microscopy images from those degraded by noise, blur, or undersampling. However, most existing deep networks are task-specific and trained on limited, homogeneously distributed data, which restricts their generalizability and practicality in biological research. Here, we present FluoResFM, a foundation model designed for multi-task and cross-distribution fluorescence microscopy image restoration in a unified model. FluoResFM leverages textual prior information (i.e., task type, imaging object, and imaging condition) to adapt the model to specific task and data distribution. It was trained using more than 4.3 million paired patches across three tasks (image denoising, deconvolution, and super-resolution) and over 20 biological structure types. FluoResFM demonstrates superior restoration performance and enhanced generalization, delivering high-fidelity reconstruction results across the three tasks and diverse internal and unseen external datasets encompassing varied biological structures and imaging conditions. Leveraging its strong generalization capability, FluoResFM can further improve its performance on unseen data through fine-tuning with only a single sample, achieving results comparable to those of conventional deep networks trained on hundreds of samples. Furthermore, the performance of existing cell/organelle segmentation models can be further improved using the high-quality image restored by FluoResFM. To make FluoResFM widely accessible to the biology research community, we developed a user-friendly napari plugin. These establish FluoResFM as a versatile foundation model for fluorescence microscopy image processing and analysis. Biological sciences/Biological techniques/Imaging/Fluorescence imaging Biological sciences/Computational biology and bioinformatics/Image processing Biological sciences/Computational biology and bioinformatics/Machine learning Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementaryinformation.pdf Supplementary information SupplementaryTable5.xlsx Supplementary Table 5 Cite Share Download PDF Status: Published Journal Publication published 10 Mar, 2026 Read the published version in Nature Communications → 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-7431973\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":507122204,\"identity\":\"1f77d92f-8289-44af-b44c-00e911a6b668\",\"order_by\":0,\"name\":\"Shenghua 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