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.

翻译：三维显微镜成像常受限于各向异性空间分辨率，导致轴向分辨率低于横向分辨率。当前利用深度神经网络的最先进各向同性重建方法在固定成像设定下可实现出色的超分辨率性能，但在实际应用中因面对未知各向异性因素时产生的伪影和模糊问题导致性能下降。为解决这些问题，我们提出DiffuseIR——一种基于扩散模型的无监督各向同性重建方法。首先，我们预训练一个扩散模型，从横向显微图像中学习生物组织的结构分布，从而生成天然高分辨率图像。随后，利用低轴向分辨率显微图像作为条件约束扩散模型的生成过程，产出高轴向分辨率重建结果。由于扩散模型学习的是独立于轴向分辨率的生物组织通用结构分布，DiffuseIR无需重新训练即可将具有未知低轴向分辨率的图像重建为高轴向分辨率图像。实验表明，所提出的DiffuseIR在电子显微镜数据上达到了最先进性能，甚至可与有监督方法相媲美。