Real-time adaptive coded aperture: Application to the compressive spectral imaging system

Yuhan Zhang*, Tingfa Xu, Xi Wang, Chenguang Pan, Jianhua Hao, Chen Huang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Adaptive coding is a recently emerging strategy for designing coded apertures using a-priori information of target layout to improve the reconstruction accuracy of compressed information. Based on the compressive sensing imaging system, this paper proposes a method for designing adaptive coded apertures by using the low-resolution a-prior information of every spectral band of the scene. This method also enables coded apertures containing both positive and negative coding information can be implemented in a compressive spectral imaging system that uses DMD or similar device as a coding device. Compared with other designs that utilized various kinds of a-prior information, this solution does not require additional equipment and is in real-time. The simulation results show the proposed real-time adaptive coded apertures method can attain better reconstruction performance than that of a random coding method at relative high compression ratio.

Original languageEnglish
Title of host publicationOptics, Photonics and Digital Technologies for Imaging Applications VI
EditorsPeter Schelkens, Tomasz Kozacki
PublisherSPIE
ISBN (Electronic)9781510634787
DOIs
Publication statusPublished - 2020
EventOptics, Photonics and Digital Technologies for Imaging Applications VI 2020 - Virtual, Online, France
Duration: 6 Apr 202010 Apr 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11353
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceOptics, Photonics and Digital Technologies for Imaging Applications VI 2020
Country/TerritoryFrance
CityVirtual, Online
Period6/04/2010/04/20

Keywords

  • Adaptive coded aperture
  • Coded aperture imaging
  • Compressive sensing
  • Computational imaging
  • Multispectral and hyperspectral imaging

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