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Compressive Hyperspectral Imaging With Side Information
2015
IEEE Journal on Selected Topics in Signal Processing
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
doi:10.1109/jstsp.2015.2411575
fatcat:v54lrrop5nhshlcyrzdaltjua4