Compressive hyperspectral imaging with side information

Xin Yuan, Tsung Han Tsai, Ruoyu Zhu, Patrick Llull, David Brady, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

150 Scopus citations


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.

Original languageEnglish (US)
Article number2411575
Pages (from-to)964-976
Number of pages13
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number6
StatePublished - Sep 1 2015
Externally publishedYes


  • Bayesian shrinkage
  • Compressive sensing
  • blind compressive sensing
  • coded aperture snapshot spectral imaging (CASSI)
  • computational photography
  • dictionary learning
  • hyperspectral image
  • side information
  • spatial light modulation

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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