Large-scale phase retrieval

Xuyang Chang; Liheng Bian; Jun Zhang

doi:10.1186/s43593-021-00004-w

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Large-scale phase retrieval

光：快讯（英文） 2021年1卷第1期页码：1-4

Research Article | Updated：2021-09-03

- Large-scale phase retrieval
- Large-scale phase retrieval
- Affiliations：
  
  School of Information and Electronics & Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China
- Author bio：
  
  *bian@bit.edu.cn
- Funds：
  
  National Natural Science Foundation of China(61971045;61827901;61991451);National Key R&D Program(2020YFB0505601);Fundamental Research Funds for the Central Universities(3052019024)
- DOI：10.1186/s43593-021-00004-w
  中图分类号：
- 纸质出版日期：2021-12，
  
  网络出版日期：2021-09-03，
  
  收稿日期：2021-04-28，
  
  修回日期：2021-06-24，
  
  录用日期：2021-07-13
- Accepted：
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Large-scale phase retrieval[J]. 光：快讯（英文）, 2021,1(1):1-4.

Xuyang Chang, Liheng Bian, Jun Zhang. Large-scale phase retrieval[J]. eLight, 2021,1(1):1-4.
Large-scale phase retrieval[J]. 光：快讯（英文）, 2021,1(1):1-4. DOI： 10.1186/s43593-021-00004-w.

Xuyang Chang, Liheng Bian, Jun Zhang. Large-scale phase retrieval[J]. eLight, 2021,1(1):1-4. DOI： 10.1186/s43593-021-00004-w.

摘要

Abstract

High-throughput computational imaging requires efficient processing algorithms to retrieve multi-dimensional and multi-scale information. In computational phase imaging

phas

e retrieval (PR) is required to reconstruct both amplitude and phase in complex space from intensity-only measurements. The existing PR algorithms suffer from the tradeoff among low computational complexity

robustness to measurement noise and strong generalization on different modalities. In this work

we report an efficient large-scale phase retrieval technique termed as

LPR

. It extends the plug-and-play generalized-alternating-projection framework from real space to nonlinear complex space. The alternating projection solver and enhancing neural network are respectively derived to tackle the measurement formation and statistical prior regularization. This framework compensates the shortcomings of each operator

so as to realize high-fidelity phase retrieval with low computational complexity and strong generalization. We applied the technique for a series of computational phase imaging modalities including coherent diffraction imaging

coded diffraction pattern imaging

and Fourier ptychographic microscopy. Extensive simulations and experiments validate that the technique outperforms the existing PR algorithms with as much as 17dB enhancement on signal-to-noise ratio

and more than one order-of-magnitude increased running efficiency. Besides

we for the first time demonstrate ultra-large-scale phase retrieval at the 8K level (pixels) in minute-level time.

关键词

Keywords

Phase retrievalComputational imagingPhase imagingLarge scale

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