Coded Hyperspectral Imaging and Blind Compressive Sensing

Ajit Rajwade

Abstract:

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. Comparisons are made between the proposed algorithm
and other techniques employed in compressive sensing, dictionary learning and matrix factorization.