Solving Blind Inverse Problem in Microscopy: Diffusion-based Zero-shot Isotropic Reconstruction

Volumetric fluorescence microscopy is crucial for non-invasive three-dimension (3D) visualization of biological systems but faces challenges due to anisotropic blurring caused by the point spread function (PSF). Previous methods have struggled with adapting to the diverse PSFs and have not effectively addressed their overall impacts of PSF. We propose Isotropic Diffusion Posterior Sampling (IsotropicDPS), solving isotropic reconstruction as a blind inverse problem. Our method employs two specialized score-based diffusion models, each trained on high-resolution lateral images and a diverse set of blurring PSFs. This approach enables the joint estimation of both the clean axial images and the PSF through a conditional posterior sampling strategy with a parallel reverse diffusion process. Remarkably, IsotropicDPS achieves zero-shot reconstruction and PSF estimation without requiring axial images during training. We validated our method through experiments on synthetic and real data, demonstrating superior performance and adaptability to varying PSF scenarios compared to existing methods.