  This paper considers a compressive sensing (CS) approach for hyperspectral
data acquisition, which results in a practical compression ratio substantially
higher than the state-of-the-art. Applying simultaneous low-rank and
joint-sparse (L&S) model to the hyperspectral data, we propose a novel
algorithm to joint reconstruction of hyperspectral data based on loopy belief
propagation that enables the exploitation of both structured sparsity and
amplitude correlations in the data. Experimental results with real
hyperspectral datasets demonstrate that the proposed algorithm outperforms the
state-of-the-art CS-based solutions with substantial reductions in
reconstruction error.
