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

Abstract : 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.
Type de document :
Communication dans un congrès
The 37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012), Mar 2012, Kyoto, Japan. 2011
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https://hal.inria.fr/hal-00705915
Contributeur : Jules Espiau de Lamaestre <>
Soumis le : vendredi 8 juin 2012 - 15:02:52
Dernière modification le : lundi 2 octobre 2017 - 16:06:02

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  • HAL Id : hal-00705915, version 1

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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. 2011. 〈hal-00705915〉

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