Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and outlook on how to better use DL to improve the reliability and efficiency in PR. Furthermore, we present a live-updating resource (https://github.com/kqwang/phase-recovery) for readers to learn more about PR.

翻译：相位恢复（PR）是指从光场的强度测量值计算其相位。从定量相位成像、相干衍射成像到自适应光学，PR 对于重建物体的折射率分布或形貌以及校正成像系统的像差至关重要。近年来，深度学习（DL）通常通过深度神经网络实现，为计算成像提供了前所未有的支持，从而为各种 PR 问题提供了更高效的解决方案。在本文中，我们首先简要介绍传统的 PR 方法。然后，我们从预处理、处理中和后处理三个阶段回顾了 DL 如何为 PR 提供支持。我们还回顾了 DL 在相位图像处理中的应用。最后，我们总结了 DL 在 PR 方面的工作，并展望了如何更好地利用 DL 来提高 PR 的可靠性和效率。此外，我们还为读者提供了一个实时更新的资源（https://github.com/kqwang/phase-recovery），以了解更多关于 PR 的信息。