Deep learning has revolutionised microscopy, enabling automated means for image classification, tracking and transformation. Beyond machine vision, deep learning has recently emerged as a universal and powerful tool to address challenging and previously untractable inverse image recovery problems. In seeking accurate, learned means of inversion, these advances have transformed conventional deep learning methods to those cognisant of the underlying physics of image formation, enabling robust, efficient and accurate recovery even in severely ill-posed conditions. In this perspective, we explore the emergence of physics-informed deep learning that will enable universal and accessible computational microscopy.

深度学习彻底改变了显微镜，实现了图像分类、跟踪和转换的自动化手段。除了机器视觉之外，深度学习最近已成为解决具有挑战性和以前难以处理的逆向图像恢复问题的通用且强大的工具。在寻求准确的、学习的反演方法时，这些进步已经将传统的深度学习方法转变为那些认识到图像形成的基础物理学的方法，即使在严重不适定的条件下也能实现稳健、高效和准确的恢复。从这个角度来看，我们探索了物理知识深度学习的出现，它将实现通用和可访问的计算显微镜。