Structured Illumination Microscopy (SIM) is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits of conventional optical microscopy. Using recent advances in image up-scaling through deep learning models, we demonstrate a method to reconstruct 3D SIM image stacks with twice the axial resolution attainable through conventional SIM reconstructions. We further demonstrate our method is robust to noise and evaluate it against two-point cases and axial gratings. Finally, we discuss potential adaptions of the method to further improve resolution.

This article is part of the Theo Murphy meeting issue ‘Super-resolution structured illumination microscopy (part 1)’.

结构照明显微镜 (SIM) 是一种广泛应用的方法，可对小于传统光学显微镜衍射极限的活体和固定生物结构进行成像。利用深度学习模型在图像放大方面的最新进展，我们演示了一种重建 3D SIM 图像堆栈的方法，其轴向分辨率是传统 SIM 重建的两倍。我们进一步证明我们的方法对噪声具有鲁棒性，并针对两点情况和轴向光栅对其进行评估。最后，我们讨论了该方法的潜在适应性，以进一步提高分辨率。

本文是 Theo Murphy 会议问题“超分辨率结构照明显微镜（第 1 部分）”的一部分。