Coded hyperspectral imaging and blind compressive sensing

Ajit Rajwade, David Kittle, Tsung Han Tsai, David Brady, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

66 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)782-812
Number of pages31
JournalSIAM Journal on Imaging Sciences
Issue number2
StatePublished - 2013
Externally publishedYes


  • Beta-Bernoulli model
  • Coded aperture snapshot spectral imager (CASSI)
  • Dictionary learning
  • Hyperspectral images
  • Image reconstruction
  • Non-parametric Bayesian
  • Projective transformation

ASJC Scopus subject areas

  • General Mathematics
  • Applied Mathematics


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