Journal Article Review Epublish 75 citations
PubMed: 38161203 DOI: 10.1038/s41377-023-01340-x
On the use of deep learning for phase recovery.
Light Sci Appl 13(1):4
Authors: Kaiqiang Wang, Li Song, Chutian Wang, Zhenbo Ren, Guangyuan Zhao, Jiazhen Dou, Jianglei Di, George Barbastathis, Renjie Zhou, Jianlin Zhao, Edmund Y Lam
Affiliations
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China. School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China. Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China.
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China.
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China.
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China.
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China.
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 provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource ( https://github.com/kqwang/phase-recovery ) for readers to learn more about PR.
MeSH
Recycling, Deep Learning, Work
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