A foundation model for multi-task cross-distribution restoration of fluorescence microscopy image

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.