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Hyperspectral image compressed sensing via low-rank and joint-sparse matrix recovery

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2 Author(s)
Golbabaee, M. ; Signal Process. Inst., Ecole Polytech. Fed. de Lausanne (EPFL), Lausanne, Switzerland ; Vandergheynst, P.

We propose a novel approach to reconstruct Hyperspectral images from very few number of noisy compressive measurements. Our reconstruction approach is based on a convex minimization which penalizes both the nuclear norm and the ℓ2,1 mixed-norm of the data matrix. Thus, the solution tends to have a simultaneous low-rank and joint-sparse structure. We explain how these two assumptions fit 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.

Published in:

Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on

Date of Conference:

25-30 March 2012

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