@inproceedings{63d860526f614028a694330a2171f67a,
title = "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 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.",
author = "Mohammad Golbabaee and Pierre Vandergheynst",
year = "2012",
month = aug,
day = "31",
doi = "10.1109/ICASSP.2012.6288484",
language = "English",
isbn = "978-1-4673-0045-2",
volume = "2012",
series = "IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
publisher = "IEEE",
booktitle = "2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
address = "USA United States",
}