DMCA
Hyperspectral image compressed sensing via low-rank and joint-sparse matrix recovery
| Venue: | ICASSP |
| Citations: | 12 - 1 self |
BibTeX
@ARTICLE{Golbabaee_hyperspectralimage,
author = {Mohammad Golbabaee and Pierre V},
title = {Hyperspectral image compressed sensing via low-rank and joint-sparse matrix recovery},
journal = {ICASSP},
year = {},
pages = {489--509}
}
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Abstract
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-ofthe-art tradeoffs between the reconstruction error and the required number of CS measurements. Index Terms — Hyperspectral images, Compressed sensing, Joint sparse signals, Low rank matrix recovery, Nuclear norm
Keyphrases
hyperspectral image joint-sparse matrix recovery nuclear norm required number index term hyperspectral image convex minimization severals simulation noisy compressive measurement reconstruction approach reconstruction error matrix recovery reconstruction scheme joint sparse signal novel approach hyperspectral data data matrix joint-sparse structure c measurement state-ofthe-art tradeoff
