Phase imaging with an untrained neural network

Fei Wang; Yaoming Bian; Haichao Wang; Meng Lyu; Giancarlo Pedrini; Wolfgang Osten; George Barbastathis; Guohai Situ

doi:10.1038/s41377-020-0302-3

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Phase imaging with an untrained neural network

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- Phase imaging with an untrained neural network
- Phase imaging with an untrained neural network
- LSA 2020年9卷第3期页码：499-505
- Affiliations：
  
  1.Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, 201800 Shanghai, China
  2.Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, 100049 Beijing, China
  3.Institut für Technische Optik, Universität Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany
  4.Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139-4301, USA
  5.Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, 310024 Hangzhou, China
- Author bio：
  
  Guohai Situ (ghsitu@siom.ac.cn)
- Funds：
- DOI：10.1038/s41377-020-0302-3
  中图分类号：
- 纸质出版日期：2020，
  
  网络出版日期：2020-05-06，
  
  收稿日期：2019-11-15，
  
  修回日期：2020-03-17，
  
  录用日期：2020-03-23
- Accepted：
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Phase imaging with an untrained neural network[J]. LSA, 2020,9(3):499-505.

Wang, F. et al. Phase imaging with an untrained neural network. Light: Science & Applications, 9, 499-505 (2020).
Phase imaging with an untrained neural network[J]. LSA, 2020,9(3):499-505. DOI： 10.1038/s41377-020-0302-3.

Wang, F. et al. Phase imaging with an untrained neural network. Light: Science & Applications, 9, 499-505 (2020). DOI： 10.1038/s41377-020-0302-3.

摘要

Abstract

Most of the neural networks proposed so far for computational imaging (CI) in optics employ a supervised training strategy

and thus need a large training set to optimize their weights and biases. Setting aside the requirements of environmental and system stability during many hours of data acquisition

in many practical applications

it is unlikely to be possible to obtain sufficient numbers of ground-truth images for training. Here

we propose to overcome this limitation by incorporating into a conventional deep neural network a complete physical model that represents the process of image formation. The most significant advantage of the resulting physics-enhanced deep neural network (PhysenNet) is that it can be used without training beforehand

thus eliminating the need for tens of thousands of labeled data. We take single-beam phase imaging as an example for demonstration. We experimentally show that one needs only to feed PhysenNet a single diffraction pattern of a phase object

and it can automatically optimize the network and eventually produce the object phase through the interplay between the neural network and the physical model. This opens up a new paradigm of neural network design

in which the concept of incorporating a physical model into a neural network can be generalized to solve many other CI problems.

关键词

Keywords

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