On the use of deep learning for phase recovery

TL;DR

This paper reviews the application of deep learning techniques across different stages of phase recovery, highlighting recent advances and future directions for improving computational imaging methods.

Contribution

It provides a comprehensive overview of how deep learning enhances phase recovery processes, including pre-processing, in-processing, and post-processing stages, and offers a resource for ongoing learning.

Findings

Deep learning improves efficiency of phase recovery methods.

DL supports phase image processing and enhances reconstruction accuracy.

The review identifies future challenges and opportunities in DL-based phase recovery.

Abstract

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.