Fluorescence microscopy is a key driver of discoveries in the life sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how content-aware image restoration based on deep learning extends the range of biological phenomena observable by microscopy. We demonstrate on eight concrete examples how microscopy images can be restored even if 60-fold fewer photons are used during acquisition, how near isotropic resolution can be achieved with up to tenfold under-sampling along the axial direction, and how tubular and granular structures smaller than the diffraction limit can be resolved at 20-times-higher frame rates compared to state-of-the-art methods. All developed image restoration methods are freely available as open source software in Python, FIJI, and KNIME.

荧光显微镜是生命科学发现的关键驱动力，可观察的现象受到显微镜光学、荧光团化学和样品耐受的最大光子暴露的限制。这些限制需要在成像速度、空间分辨率、曝光和成像深度之间进行权衡。在这项工作中，我们展示了基于深度学习的内容感知图像恢复如何扩展显微镜可观察的生物现象的范围。我们通过八个具体示例演示了即使在采集过程中使用的光子数减少了 60 倍，如何恢复显微图像、如何通过沿轴向方向进行多达十倍的欠采样来实现接近各向同性的分辨率，以及管状和粒状结构如何变得更小与最先进的方法相比，可以在高 20 倍的帧速率下解析超出衍射极限的图像。所有开发的图像恢复方法都可以作为 Python、FIJI 和 KNIME 的开源软件免费提供。