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

eLight Vol. 1, Issue 1, Pages: 1-4(2021)

RESEARCH ARTICLE | Updated：2021-09-03

- 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：
- DOI：10.1186/s43593-021-00004-w
  CLC：
- Published：2021-12，
  
  Published Online：03 September 2021，
  
  Received：28 April 2021，
  
  Revised：24 June 2021，
  
  Accepted：13 July 2021
- Accepted：
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Xuyang Chang, Liheng Bian, Jun Zhang. Large-scale phase retrieval. [J]. eLight, 2021,1(1):1-4.
DOI：

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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