  Electron microscopy (EM) images exhibit anisotropic axial resolution due to
the characteristics inherent to the imaging modality, presenting challenges in
analysis and downstream tasks.In this paper, we propose a diffusion-model-based
framework that overcomes the limitations of requiring reference data or prior
knowledge about the degradation process. Our approach utilizes 2D diffusion
models to consistently reconstruct 3D volumes and is well-suited for highly
downsampled data. Extensive experiments conducted on two public datasets
demonstrate the robustness and superiority of leveraging the generative prior
compared to supervised learning methods. Additionally, we demonstrate our
method's feasibility for self-supervised reconstruction, which can restore a
single anisotropic volume without any training data.
