FM2S: Towards Spatially-correlated Noise Modeling in Zero-shot Fluorescence Microscopy Image Denoising

Fluorescence microscopy image (FMI) denoising faces critical challenges because of the compound mixed Poisson-Gaussian noise with strong spatial correlation and the impracticality of acquiring paired noisy/clean data in dynamic biomedical scenarios. While supervised methods trained on synthetic noise (e.g., Gaussian/Poisson) suffer from out-of-distribution generalization issues, existing self-supervised approaches degrade under real FMI noise because they oversimplify noise assumptions and computationally intensive deep architectures. In this work, we propose fluorescence micrograph to self (FM2S), a zero-shot denoiser that achieves efficient FMI denoising through three key innovations: 1) A noise injection module that ensures training data sufficiency through adaptive Poisson-Gaussian synthesis while preserving spatial correlation and global statistics of FMI noise for robust model generalization; 2) A two-stage proactive learning strategy that first recovers structural priors via predenoised targets and then refines high-frequency details through noise distribution alignment; 3) An ultralight-weight network (3.5 k parameters) enabling rapid convergence with 270× faster training and inference than state-of-the-art (SOTA). Extensive experiments across FMI datasets demonstrate FM2S′ superiority: It outperforms CVF-SID by 1.4 dB in peak signal-to-noise ratio (PSNR) on average while requiring 0.1% of the parameters of the AP-BSN. Notably, FM2S maintains stable performance across varying noise levels, indicating its practicality for microscopy platforms with diverse sensor characteristics. The code and datasets can be found at https://github.com/Danielement321/FM2S.