HYPERSPECTRAL IMAGE COMPRESSED SENSING VIA LOW-RANK AND JOINT-SPARSE MATRIX RECOVERY

Mohammad Golbabaee (1) , Pierre Vandergheynst (1)

1 LTS2 - EPFL

We 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.

Mots clés: Hyperspectral images; Compressed sensing; Joint sparse signals; Low rank matrix recovery; Nuclear norm

Mohammad Golbabaee, Pierre Vandergheynst. HYPERSPECTRAL IMAGE COMPRESSED SENSING VIA LOW-RANK AND JOINT-SPARSE MATRIX RECOVERY. The 37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012), Mar 2012, Kyoto, Japan. ⟨hal-00705915⟩