Golbabaee, MohammadVandergheynst, Pierre2011-10-072011-10-072011-10-07201210.1109/ICASSP.2012.6288484https://infoscience.epfl.ch/handle/20.500.14299/71481WOS:000312381402206We propose a novel approach to reconstruct Hyperspectral images from very few number of noisy compressive measure- ments. Our reconstruction approach is based on a convex minimiza- tion which penalizes both the nuclear norm and the l2,1 mixed-norm of the data matrix. Thus, the solution tends to have a simultane- ously low-rank and joint-sparse structure. We explain how these two assumptions fit the Hyperspectral data, and by severals simulations we show that our proposed reconstruction scheme significantly enhances the state-of-the-art tradeoffs between the reconstruction error and the required number of CS measurements.Hyperspectral imagesCompressed sensingJoint sparse signalsLow rank matrix recoveryNuclear normLTS2text::conference output::conference proceedings::conference paper