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.


Published in:
2012 Ieee International Conference On Acoustics, Speech And Signal Processing (Icassp), 2741-2744
Presented at:
The 37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012)
Year:
2012
Publisher:
New York, Ieee
ISBN:
978-1-4673-0046-9
Keywords:
Laboratories:




 Record created 2011-10-07, last modified 2018-09-13

n/a:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)