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

Mohammad Golbabaee, Pierre Vandergheynst

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

62 Citations (SciVal)

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 ℓ 2,1 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-of-the-art tradeoffs between the reconstruction error and the required number of CS measurements.
Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Volume2012
ISBN (Electronic)978-1-4673-0046-9, 978-1-4673-0044-5
ISBN (Print)978-1-4673-0045-2
DOIs
Publication statusPublished - 31 Aug 2012

Publication series

NameIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
ISSN (Print)1520-6149

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