Compressive hyperspectral imaging with side information

Published

Journal Article

© 2007-2012 IEEE. A blind compressive sensing algorithm is proposed to reconstruct hyperspectral images from spectrally-compressed measurements. The wavelength-dependent data are coded and then superposed, mapping the three-dimensional hyperspectral datacube to a two-dimensional image. The inversion algorithm learns a dictionary in situ from the measurements via global-local shrinkage priors. By using RGB images as side information of the compressive sensing system, the proposed approach is extended to learn a coupled dictionary from the joint dataset of the compressed measurements and the corresponding RGB images, to improve reconstruction quality. A prototype camera is built using a liquid-crystal-on-silicon modulator. Experimental reconstructions of hyperspectral datacubes from both simulated and real compressed measurements demonstrate the efficacy of the proposed inversion algorithm, the feasibility of the camera and the benefit of side information.

Full Text

Duke Authors

Cited Authors

  • Yuan, X; Tsai, TH; Zhu, R; Llull, P; Brady, D; Carin, L

Published Date

  • September 1, 2015

Published In

Volume / Issue

  • 9 / 6

Start / End Page

  • 964 - 976

International Standard Serial Number (ISSN)

  • 1932-4553

Digital Object Identifier (DOI)

  • 10.1109/JSTSP.2015.2411575

Citation Source

  • Scopus