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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 52, Iss. 10 — Apr. 1, 2013
  • pp: D46–D54

Compressive hyperspectral imaging by random separable projections in both the spatial and the spectral domains

Yitzhak August, Chaim Vachman, Yair Rivenson, and Adrian Stern  »View Author Affiliations

Applied Optics, Vol. 52, Issue 10, pp. D46-D54 (2013)

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An efficient method and system for compressive sensing of hyperspectral data is presented. Compression efficiency is achieved by randomly encoding both the spatial and the spectral domains of the hyperspectral datacube. Separable sensing architecture is used to reduce the computational complexity associated with the compressive sensing of a large volume of data, which is typical of hyperspectral imaging. The system enables optimizing the ratio between the spatial and the spectral compression sensing ratios. The method is demonstrated by simulations performed on real hyperspectral data.

© 2013 Optical Society of America

OCIS Codes
(110.4190) Imaging systems : Multiple imaging
(110.1758) Imaging systems : Computational imaging
(110.4155) Imaging systems : Multiframe image processing
(110.4234) Imaging systems : Multispectral and hyperspectral imaging

Original Manuscript: November 5, 2012
Revised Manuscript: February 18, 2013
Manuscript Accepted: February 21, 2013
Published: March 22, 2013

Yitzhak August, Chaim Vachman, Yair Rivenson, and Adrian Stern, "Compressive hyperspectral imaging by random separable projections in both the spatial and the spectral domains," Appl. Opt. 52, D46-D54 (2013)

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