Hyperspectral Image Compressed Sensing Via Low-Rank And Joint-Sparse Matrix Recovery

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.


Publié dans:
2012 Ieee International Conference On Acoustics, Speech And Signal Processing (Icassp), 2741-2744
Présenté à:
The 37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012)
Année
2012
Publisher:
New York, Ieee
ISBN:
978-1-4673-0046-9
Mots-clefs:
Laboratoires:




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