@inproceedings{41bdbd54316d40248424f331a08729c2,
title = "Fast holo-kronecker compressive sensing for hyperspectral image",
abstract = "Compressive sensing of hyperspectral image (HSI) faces the difficulties of complex computation and much information redundancies. In this paper, we propose a highly-efficient compressive sensing framework including sampling method and its corresponding reconstruction algorithm for HSI. Kronecker product is used to generate the sparsifying basis and measurement matrices. Both the data in spatial dimensions and spectral dimension are compressed, resulting an enhanced sampling efficiency. Very few measurements are needed for a successful reconstruction. We combine the sparsity model and low multilinear-rank model for fast and accurate reconstruction. Iterative algorithm is employed to reconstruct the data only in one dimension of HSI independently instead of all dimensions globally, which can speed up the reconstruction.",
keywords = "Compressive sensing, Hyperspectral image, Kronecker product, Low multilinear-rank",
author = "Rongqiang Zhao and Qiang Wang and Yi Shen",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 10th International Conference on Communications and Networking in China, CHINACOM 2015 ; Conference date: 15-08-2015 Through 17-08-2015",
year = "2016",
month = jun,
day = "22",
doi = "10.1109/CHINACOM.2015.7497984",
language = "英语",
series = "Proceedings of the 2015 10th International Conference on Communications and Networking in China, CHINACOM 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "460--464",
booktitle = "Proceedings of the 2015 10th International Conference on Communications and Networking in China, CHINACOM 2015",
address = "美国",
}