MicroFM: Physics-guided Flow Matching for Isotropic Microscopy Reconstruction

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 based approaches reduce acquisition burden, but common synthetic pipelines blur with Gaussian kernels that do not match the physical degradation, and many methods lack explicit volumetric geometry constraints since they process 2D slices independently. These gaps lead to 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 the first flow-matching framework for 3D 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 performance, producing sharper structures, more isotropic spectra, and substantial gains in both full-reference and no-reference metrics.