Coded hyperspectral imaging and blind compressive sensing

Published

Journal Article

Blind compressive sensing (CS) is considered for reconstruction of hyperspectral data imaged by a coded aperture camera. The measurements are manifested as a superposition of the coded wavelength-dependent data, with the ambient three-dimensional hyperspectral datacube mapped to a two-dimensional measurement. The hyperspectral datacube is recovered using a Bayesian implementation of blind CS. Several demonstration experiments are presented, including measurements performed using a coded aperture snapshot spectral imager (CASSI) camera. The proposed approach is capable of efficiently reconstructing large hyperspectral datacubes. Comparisons are made between the proposed algorithm and other techniques employed in compressive sensing, dictionary learning, and matrix factorization. © 2013 Society for Industrial and Applied Mathematics.

Full Text

Duke Authors

Cited Authors

  • Rajwade, A; Kittle, D; Tsai, TH; Brady, D; Carin, L

Published Date

  • July 15, 2013

Published In

Volume / Issue

  • 6 / 2

Start / End Page

  • 782 - 812

Electronic International Standard Serial Number (EISSN)

  • 1936-4954

Digital Object Identifier (DOI)

  • 10.1137/120875302

Citation Source

  • Scopus