Thursday, February 07, 2013

Coded Hyperspectral Imaging and Blind Compressive Sensing

Now we're getting to the heart of calibration of compressive imagers by extending dictionary learning to blind Compressive Sensing using the CASSI imager. I love it and look forward to the attendant algorithm.

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.

1 comment:

Anonymous said...

Is BCS being used today for real life medical examination?