MicroFM: Physics-guided Flow Matching for Isotropic Microscopy Reconstruction

Abstract

Isotropic microscopy reconstruction remains challenging because the anisotropic point spread function in optical systems yields much poorer axial resolution and hampers accurate 3D analysis. Hardware strategies can approach isotropy, yet they are complex, costly, susceptible to sidelobes, and introduce phototoxicity. Deep learning approaches reduce acquisition burden, yet synthetic pipelines often rely on Gaussian blur mismatched to physical degradation, and many methods lack explicit volumetric geometry constraints as they process 2D slices independently, resulting in low-fidelity reconstructions. To address these challenges, we present MicroFM, which synthesizes realistic training data using physical PSFs matched to the target microscope. MicroFM also introduces a physics-guided flow-matching framework for isotropic microscopy reconstruction, guided by a continuous implicit geometry prior to achieve high-fidelity isotropic recovery. Across four fluorescence microscopy systems and datasets, MicroFM achieves state-of-the-art (SOTA) performance, producing sharper structures, more isotropic spectra, and substantial gains in both full-reference and no-reference metrics.