Compressive Hyperspectral Imaging With Side Information

A blind compressive sensing algorithm is proposed to reconstruct hyperspectral images from spectrally-compressed measurements. The wavelength-dependent data are coded and then superposed, mapping the three-dimensional hyperspectral datacube to a two-dimensional image. The inversion algorithm learns...

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Bibliographic Details
Published in:IEEE journal of selected topics in signal processing 2015-09, Vol.9 (6), p.964-976
Main Authors: Xin Yuan, Tsung-Han Tsai, Ruoyu Zhu, Llull, Patrick, Brady, David, Carin, Lawrence
Format: Article
Language:English
Subjects:
Bayesian shrinkage
blind compressive sensing
Cameras
coded aperture snapshot spectral imaging (CASSI)
Compressive sensing
computational photography
Detectors
Dictionaries
dictionary learning
hyperspectral image
Hyperspectral imaging
Image coding
Image reconstruction
side information
spatial light modulation
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Summary:A blind compressive sensing algorithm is proposed to reconstruct hyperspectral images from spectrally-compressed measurements. The wavelength-dependent data are coded and then superposed, mapping the three-dimensional hyperspectral datacube to a two-dimensional image. The inversion algorithm learns a dictionary in situ from the measurements via global-local shrinkage priors. By using RGB images as side information of the compressive sensing system, the proposed approach is extended to learn a coupled dictionary from the joint dataset of the compressed measurements and the corresponding RGB images, to improve reconstruction quality. A prototype camera is built using a liquid-crystal-on-silicon modulator. Experimental reconstructions of hyperspectral datacubes from both simulated and real compressed measurements demonstrate the efficacy of the proposed inversion algorithm, the feasibility of the camera and the benefit of side information.
ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2015.2411575