Compressive Hyperspectral Imaging with Side Information
This work addresses hyperspectral imaging reconstruction for applications like remote sensing or medical imaging, but it is incremental as it builds on existing compressive sensing methods with side information.
The authors tackled the problem of reconstructing hyperspectral images from compressed measurements by proposing a blind compressive sensing algorithm that uses RGB images as side information to improve reconstruction quality, demonstrating its efficacy through experimental reconstructions from both simulated and real measurements.
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 {\em 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.