ArticleNature methods2024
Pretraining a foundation model for generalizable fluorescence microscopy-based image restoration.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Accelerating Stroke MRI With Diffusion Probabilistic Models Through Large-Scale Pre-Training and Target-Specific Fine-Tuning.Magnetic resonance in medicine · 2026Article
- AI-empowered super-resolution microscopy: a revolution in nanoscale cellular imaging.Nature methods · 2026Review
- Deep Learning for Localization Microscopy in 2D and 3D.Accounts of chemical research · 2026Article
- Generalist biological artificial intelligence in modeling the language of life.Nature biotechnology · 2026Review
- HiCP2GAN: A Plug and Play Foundation Model-based GAN for Hi-C Enhancement.bioRxiv : the preprint server for biology · 2026Article
- Pushing the limits of fluorescence imaging with a restoration neural network aggregating large-view statistics.Nature communications · 2026Article
- A foundation model for multi-task cross-distribution restoration of fluorescence microscopy images.Nature communications · 2026Article
- A Self-Supervised Foundation Model Based on Three-Dimensional Chest CT Scans for Lung Cancer Diagnosis and Prognosis Prediction.Radiology. Imaging cancer · 2026Article
- Self-adaptive fine-tuning of deep learning super-resolution microscopy for artifact suppression in live-cell imaging.Innovation (Cambridge (Mass.)) · 2026Article
- Enhanced Precision of Fluorescence In Situ Hybridization (FISH) Analysis Using Neural Network-Based Nuclear Segmentation for Digital Microscopy Samples.Sensors (Basel, Switzerland) · 2026Article
- DeBCR: a sparsity-efficient framework for image enhancement through a deep-learning-based solution to inverse problems.Communications engineering · 2026Article
- A comprehensive foundation model for cryo-EM image processing.Nature methods · 2026Article
- Biophysical simulation enables segmentation and nervous system atlas mapping for image first spatial omics.NPJ systems biology and applications · 2025Article
- AutoQC-Bench: a diffusion model and benchmark for automatic quality control in high-throughput microscopy.Npj imaging · 2025Article
- URFM: A general Ultrasound Representation Foundation Model for advancing ultrasound image diagnosis.iScience · 2025Article
- Calling all data.Nature methods · 2025Article
- Rethinking deep learning in bioimaging through a data centric lens.Npj imaging · 2025Article
- A large-scale curated and filterable dataset for cryo-EM foundation model pre-training.Scientific data · 2025Article
- Dark sectioning boosts segmentation accuracy and image quality in fluorescence microscopy.Nature methods · 2025Article
- Single Molecule Localization Super-resolution Dataset for Deep Learning with Paired Low-resolution Images.Scientific data · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Fluorescence microscopy-based image restoration has received widespread attention in the life sciences and has led to significant progress, benefiting from deep learning technology. However, most current task-specific methods have limited generalizability to different fluorescence microscopy-based image restoration problems. Here, we seek to improve generalizability and explore the potential of applying a pretrained foundation model to fluorescence microscopy-based image restoration. We provide a universal fluorescence microscopy-based image restoration (UniFMIR) model to address different restoration problems, and show that UniFMIR offers higher image restoration precision, better generalization and increased versatility. Demonstrations on five tasks and 14 datasets covering a wide range of microscopy imaging modalities and biological samples demonstrate that the pretrained UniFMIR can effectively transfer knowledge to a specific situation via fine-tuning, uncover clear nanoscale biomolecular structures and facilitate high-quality imaging. This work has the potential to inspire and trigger new research highlights for fluorescence microscopy-based image restoration.
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Registered trials
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