Learning to synthesize: robust phase retrieval at low photon counts

Mo Deng; Shuai Li; Alexandre Goy; Iksung Kang; George Barbastathis

doi:10.1038/s41377-020-0267-2

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Learning to synthesize: robust phase retrieval at low photon counts

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- Learning to synthesize: robust phase retrieval at low photon counts
- Learning to synthesize: robust phase retrieval at low photon counts
- LSA 2020年9卷第2期页码：343-358
- Affiliations：
  
  1.Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  2.Sensebrain Technology Limited LLC, 2550 N 1st Street, Suite 300, San Jose, CA 95131, USA
  3.Omnisens SA, Riond Bosson 3, 1110 Morges, VD, Switzerland
  4.Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  5.Singapore-MIT Alliance for Research and Technology (SMART) Centre, Singapore 117543, Singapore
- Author bio：
  
  Mo Deng (modeng@mit.edu)
- Funds：
- DOI：10.1038/s41377-020-0267-2
  中图分类号：
- 纸质出版日期：2020，
  
  网络出版日期：2020-03-09，
  
  收稿日期：2019-08-29，
  
  修回日期：2020-02-04，
  
  录用日期：2020-02-19
- Accepted：
Scan QR Code
Learning to synthesize: robust phase retrieval at low photon counts[J]. LSA, 2020,9(2):343-358.

Deng, M. et al. Learning to synthesize: robust phase retrieval at low photon counts. Light: Science & Applications, 9, 343-358 (2020).
Learning to synthesize: robust phase retrieval at low photon counts[J]. LSA, 2020,9(2):343-358. DOI： 10.1038/s41377-020-0267-2.

Deng, M. et al. Learning to synthesize: robust phase retrieval at low photon counts. Light: Science & Applications, 9, 343-358 (2020). DOI： 10.1038/s41377-020-0267-2.

摘要

Abstract

The quality of inverse problem solutions obtained through deep learning is limited by the nature of the priors learned from examples presented during the training phase. Particularly in the case of quantitative phase retrieval

spatial frequencies that are underrepresented in the training database

most often at the high band

tend to be suppressed in the reconstruction. Ad hoc solutions have been proposed

such as pre-amplifying the high spatial frequencies in the examples; however

while that strategy improves the resolution

it also leads to high-frequency artefacts

as well as low-frequency distortions in the reconstructions. Here

we present a new approach that learns separately how to handle the two frequency bands

low and high

and learns how to synthesize these two bands into full-band reconstructions. We show that this "learning to synthesize" (LS) method yields phase reconstructions of high spatial resolution and without artefacts and that it is resilient to high-noise conditions

e.g.

in the case of very low photon flux. In addition to the problem of quantitative phase retrieval

the LS method is applicable

in principle

to any inverse problem where the forward operator treats different frequency bands unevenly

i.e.

is ill-posed.

关键词

Keywords

references

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