One intrinsic yet critical issue that troubles the field of fluorescence microscopy ever since its introduction is the unmatched resolution in the lateral and axial directions (i.e., resolution anisotropy), which severely deteriorates the quality, reconstruction, and analysis of 3D volume images. By leveraging the natural anisotropy, we present a deep self-learning method termed Self-Net that significantly improves the resolution of axial images by using the lateral images from the same raw dataset as rational targets. By incorporating unsupervised learning for realistic anisotropic degradation and supervised learning for high-fidelity isotropic recovery, our method can effectively suppress the hallucination with substantially enhanced image quality compared to previously reported methods. In the experiments, we show that Self-Net can reconstruct high-fidelity isotropic 3D images from organelle to tissue levels via raw images from various microscopy platforms, e.g., wide-field, laser-scanning, or super-resolution microscopy. For the first time, Self-Net enables isotropic whole-brain imaging at a voxel resolution of 0.2 × 0.2 × 0.2 μm³, which addresses the last-mile problem of data quality in single-neuron morphology visualization and reconstruction with minimal effort and cost. Overall, Self-Net is a promising approach to overcoming the inherent resolution anisotropy for all classes of 3D fluorescence microscopy.

自从荧光显微镜问世以来，困扰其领域的一个内在而又关键的问题是横向和轴向上无与伦比的分辨率（即分辨率各向异性），这严重恶化了 3D 体积图像的质量、重建和分析。通过利用自然各向异性，我们提出了一种称为 Self-Net 的深度自学习方法，该方法通过使用来自同一原始数据集的横向图像作为理性目标，显着提高了轴向图像的分辨率。通过结合用于现实各向异性退化的无监督学习和用于高保真各向同性恢复的监督学习，我们的方法可以有效抑制幻觉，与之前报道的方法相比，图像质量显着提高。在实验中，我们表明 Self-Net 可以通过来自各种显微镜平台（例如宽视野、激光扫描或超分辨率显微镜）的原始图像重建从细胞器到组织水平的高保真各向同性 3D 图像。Self-Net首次实现了体素分辨率为0.2 × 0.2 × 0.2 μm ³的各向同性全脑成像，以最小的努力和成本解决了单神经元形态可视化和重建中数据质量的最后一英里问题。总的来说，Self-Net 是一种很有前途的方法，可以克服所有类别 3D 荧光显微镜固有的分辨率各向异性。