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

Deep learning has demonstrated remarkable abilities in restoring fluorescence microscopy images degraded by noise, blur, or undersampling. However, most existing models are task-specific and trained on limited, homogeneously distributed data, which restricts their generalizability and practicality. Here, we present FluoResFM, a foundation model for multi-task and cross-distribution fluorescence microscopy image restoration in a unified model. FluoResFM leverages textual prior information to adapt to specific tasks and data distributions. Trained on datasets across three tasks (image denoising, deconvolution, and super-resolution) and over 20 biological structures, FluoResFM demonstrates superior restoration performance and enhanced generalization across datasets with varied biological structures and imaging conditions. Through fine-tuning with only a single sample, FluoResFM can further improve its performance on unseen data, achieving results comparable to conventional models trained on hundreds of samples, and be easily adapted to additional tasks, including 3D image restoration, surface projection, isotropic reconstruction, and super-resolution with various scale factors. Moreover, the performance of existing cell/organelle segmentation models can be enhanced using the high-quality images restored by FluoResFM.