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.

本研究提出了一种盲压缩感知算法，可以从光谱压缩测量中重建高光谱图像，并使用 RGB 图像作为辅助信息来改善重建质量。实验结果验证了该算法的有效性和液晶 - 硅调制器相机的可行性。