Compressive hyperspectral imaging with side information

TitleCompressive hyperspectral imaging with side information
Publication TypeJournal Article
Year of Publication2015
AuthorsX Yuan, TH Tsai, R Zhu, P Llull, D Brady, and L Carin
JournalIeee Journal of Selected Topics in Signal Processing
Start Page964
Pagination964 - 976
Date Published09/2015

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.

Short TitleIeee Journal of Selected Topics in Signal Processing