On the use of deep learning for phase recovery

Kaiqiang Wang, Li Song, Chutian Wang, Zhenbo Ren, Guangyuan Zhao +6 more
8/2/2023
physics.opticscs.LGeess.IV

Official code: kqwang/phase-recovery (1,210 stars, MIT, last commit 2025-09-29).

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.

View on arXivView PDF

Code Implementations(1)

kqwang/phase-recoveryOfficial100%
MIT

Resources for phase recovery (also called phase imaging, phase retrieval, or phase reconstruction)

1,21073May 31, 20231 years agoMIT
computational-imagingdeep-learningholographyinterferometryphase-imaging+5 more

Similar Papers

Deep learning phase recovery: data-driven, physics-driven, or combining both?

93% match1 repo532024

Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image Reconstruction

89% match2024

Direct Zernike Coefficient Prediction from Point Spread Functions and Extended Images using Deep Learning

85% match2024

PRISTA-Net: Deep Iterative Shrinkage Thresholding Network for Coded Diffraction Patterns Phase Retrieval

85% match2023

Coordinate-based Neural Network for Fourier Phase Retrieval

85% match2023

Hybrid deep learning-based phase diversity method for wavefront reconstruction

84% match2026

Cite this paper

@article{wang2023deep,
  title  = {On the use of deep learning for phase recovery},
  author = {Kaiqiang Wang and Li Song and Chutian Wang and Zhenbo Ren and Guangyuan Zhao and Jiazhen Dou and Jianglei Di and George Barbastathis and Renjie Zhou and Jianlin Zhao and Edmund Y. Lam},
  year   = {2023},
  eprint = {2308.00942},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.00942v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2308.00942.svg)](https://codeofpaper.com/paper/2308.00942)

Discussion