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.

翻译：电子显微镜（EM）图像因成像模态固有特性而呈现各向异性轴向分辨率，给分析及下游任务带来挑战。本文提出一种基于扩散模型的框架，克服了需要参考数据或退化过程先验知识的局限性。我们的方法利用二维扩散模型一致地重建三维体数据，尤其适用于高度降采样的数据。在两个公开数据集上进行的广泛实验表明，与监督学习方法相比，利用生成先验具有鲁棒性和优越性。此外，我们还验证了该方法在自监督重建中的可行性，无需任何训练数据即可恢复单个各向异性体数据。