Learning-based real-time imaging through dynamic scattering media

Haishan Liu; Fei Wang; Ying Jin; Xianzheng Ma; Siteng Li; Yaoming Bian; Guohai Situ

doi:10.1038/s41377-024-01569-0

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Learning-based real-time imaging through dynamic scattering media

Original Articles

- Learning-based real-time imaging through dynamic scattering media
- Light: Science & Applications Vol. 13, Issue 10, Pages: 2131-2144(2024)
- 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, 100049 Beijing, China
  3.Department of Engineering Science, University of Oxford, Oxford, UK
  4.Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- Author bio：
  
  Guohai Situ (ghsitu@siom.ac.cn)
- Funds：
- DOI：10.1038/s41377-024-01569-0
  CLC：
- Received：28 April 2024，
  
  Revised：01 August 2024，
  
  Accepted：2024-08-05，
  
  Published Online：16 August 2024，
  
  Published：31 October 2024
- Accepted：
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Liu, H. S. et al. Learning-based real-time imaging through dynamic scattering media. Light: Science & Applications, 13, 2131-2144 (2024).
DOI：

Liu, H. S. et al. Learning-based real-time imaging through dynamic scattering media. Light: Science & Applications, 13, 2131-2144 (2024). DOI： 10.1038/s41377-024-01569-0.

摘要

Abstract

Imaging through dynamic scattering media is one of the most challenging yet fascinating problems in optics

with applications spanning from biological detection to remote sensing. In this study

we propose a comprehensive learning-based technique that facilitates real-time

non-invasive

incoherent imaging of real-world objects through dense and dynamic scattering media. We conduct extensive experiments

demonstrating the capability of our technique to see through turbid water and natural fog. The experimental results indicate that the proposed technique surpasses existing approaches in numerous aspects and holds significant potential for imaging applications across a broad spectrum of disciplines.

关键词

Keywords

references

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