Skip to main content

Dictionary learning for hyperspectral video compressive sensing

Publication ,  Journal Article
Carin, L
Published in: Frontiers in Optics, FIO 2012
December 1, 2012

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 wavelengthdependent 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, including hyperspectral video. Comparisons are made between the proposed algorithm and other techniques employed in compressive sensing, dictionary learning and matrix factorization. © OSA 2012.

Duke Scholars

Published In

Frontiers in Optics, FIO 2012

Publication Date

December 1, 2012
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Carin, L. (2012). Dictionary learning for hyperspectral video compressive sensing. Frontiers in Optics, FIO 2012.
Carin, L. “Dictionary learning for hyperspectral video compressive sensing.” Frontiers in Optics, FIO 2012, December 1, 2012.
Carin L. Dictionary learning for hyperspectral video compressive sensing. Frontiers in Optics, FIO 2012. 2012 Dec 1;
Carin, L. “Dictionary learning for hyperspectral video compressive sensing.” Frontiers in Optics, FIO 2012, Dec. 2012.
Carin L. Dictionary learning for hyperspectral video compressive sensing. Frontiers in Optics, FIO 2012. 2012 Dec 1;

Published In

Frontiers in Optics, FIO 2012

Publication Date

December 1, 2012