Far-field super-resolution ghost imaging with a deep neural network constraint

Fei Wang; Chenglong Wang; Mingliang Chen; Wenlin Gong; Yu Zhang; Shensheng Han; Guohai Situ

doi:10.1038/s41377-021-00680-w

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Far-field super-resolution ghost imaging with a deep neural network constraint

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- Far-field super-resolution ghost imaging with a deep neural network constraint
- Far-field super-resolution ghost imaging with a deep neural network constraint
- LSA 2022年11卷第1期页码：27-37
- Affiliations：
  
  1.Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
  2.Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
  3.Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
  4.CAS Center for Excellence in Ultra-intense Laser Science, Shanghai 201800, China
- Author bio：
  
  Guohai Situ (ghsitu@siom.ac.cn)
- Funds：
- DOI：10.1038/s41377-021-00680-w
  中图分类号：
- 纸质出版日期：2022-01-31，
  
  网络出版日期：2022-01-01，
  
  收稿日期：2021-05-17，
  
  修回日期：2021-10-28，
  
  录用日期：2021-11-14
- Accepted：
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Far-field super-resolution ghost imaging with a deep neural network constraint[J]. LSA, 2022,11(1):27-37.

Wang, F. et al. Far-field super-resolution ghost imaging with a deep neural network constraint. Light: Science & Applications, 11, 27-37 (2022).
Far-field super-resolution ghost imaging with a deep neural network constraint[J]. LSA, 2022,11(1):27-37. DOI： 10.1038/s41377-021-00680-w.

Wang, F. et al. Far-field super-resolution ghost imaging with a deep neural network constraint. Light: Science & Applications, 11, 27-37 (2022). DOI： 10.1038/s41377-021-00680-w.

摘要

Abstract

Ghost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing. However

GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image

imposing a practical limit for its applications. Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network. The resulting hybrid neural network does not need to pre-train on any dataset

and allows the reconstruction of a far-field image with the resolution beyond the diffraction limit. Furthermore

the physical model imposes a constraint to the network output

making it effectively interpretable. We experimentally demonstrate the proposed GI technique by imaging a flying drone

and show that it outperforms some other widespread GI techniques in terms of both spatial resolution and sampling ratio. We believe that this study provides a new framework for GI

and paves a way for its practical applications.

关键词

Keywords

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