Dictionary learning for hyperspectral video compressive sensing

Larry Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish (US)
Title of host publicationFrontiers in Optics, FIO 2012
PublisherOptical Society of America (OSA)custserv@osa.org
ISBN (Print)9781557529565
StatePublished - Jan 1 2012
Externally publishedYes

Bibliographical note

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