  Three-dimensional microscopy is often limited by anisotropic spatial
resolution, resulting in lower axial resolution than lateral resolution.
Current State-of-The-Art (SoTA) isotropic reconstruction methods utilizing deep
neural networks can achieve impressive super-resolution performance in fixed
imaging settings. However, their generality in practical use is limited by
degraded performance caused by artifacts and blurring when facing unseen
anisotropic factors. To address these issues, we propose DiffuseIR, an
unsupervised method for isotropic reconstruction based on diffusion models.
First, we pre-train a diffusion model to learn the structural distribution of
biological tissue from lateral microscopic images, resulting in generating
naturally high-resolution images. Then we use low-axial-resolution microscopy
images to condition the generation process of the diffusion model and generate
high-axial-resolution reconstruction results. Since the diffusion model learns
the universal structural distribution of biological tissues, which is
independent of the axial resolution, DiffuseIR can reconstruct authentic images
with unseen low-axial resolutions into a high-axial resolution without
requiring re-training. The proposed DiffuseIR achieves SoTA performance in
experiments on EM data and can even compete with supervised methods.
