Deeply learned broadband encoding stochastic hyperspectral imaging

Wenyi Zhang; Hongya Song; Xin He; Longqian Huang; Xiyue Zhang; Junyan Zheng; Weidong Shen; Xiang Hao; Xu Liu

doi:10.1038/s41377-021-00545-2

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Deeply learned broadband encoding stochastic hyperspectral imaging

Letters

- Deeply learned broadband encoding stochastic hyperspectral imaging
- Deeply learned broadband encoding stochastic hyperspectral imaging
- LSA 2021年10卷第6期页码：969-975
- Affiliations：
  
  1.State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Technology, Zhejiang University, Hangzhou 310027, China
  2.Intelligent Optics & Photonics Research Center, Jiaxing Institute of Zhejiang University, Jiaxing 314000 Zhejiang Province, China
- Author bio：
  
  Xiang Hao (haox@zju.edu.cn)
  Xu Liu (liuxu@zju.edu.cn)
- Funds：
- DOI：10.1038/s41377-021-00545-2
  中图分类号：
- 纸质出版日期：2021-06-30，
  
  网络出版日期：2021-05-25，
  
  收稿日期：2021-01-10，
  
  修回日期：2021-04-22，
  
  录用日期：2021-04-23
- Accepted：
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Deeply learned broadband encoding stochastic hyperspectral imaging[J]. LSA, 2021,10(6):969-975.

Zhang, W. Y. et al. Deeply learned broadband encoding stochastic hyperspectral imaging. Light: Science & Applications, 10, 969-975 (2021).
Deeply learned broadband encoding stochastic hyperspectral imaging[J]. LSA, 2021,10(6):969-975. DOI： 10.1038/s41377-021-00545-2.

Zhang, W. Y. et al. Deeply learned broadband encoding stochastic hyperspectral imaging. Light: Science & Applications, 10, 969-975 (2021). DOI： 10.1038/s41377-021-00545-2.

摘要

Abstract

Many applications requiring both spectral and spatial information at high resolution benefit from spectral imaging. Although different technical methods have been developed and commercially available

computational spectral cameras represent a compact

lightweight

and inexpensive solution. However

the tradeoff between spatial and spectral resolutions

dominated by the limited data volume and environmental noise

limits the potential of these cameras. In this study

we developed a deeply learned broadband encoding stochastic hyperspectral camera. In particular

using advanced artificial intelligence in filter design and spectrum reconstruction

we achieved 7000–11

000 times faster signal processing and ~10 times improvement regarding noise tolerance. These improvements enabled us to precisely and dynamically reconstruct the spectra of the entire field of view

previously unreachable with compact computational spectral cameras.

关键词

Keywords

references

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