Title :
Fast holo-kronecker compressive sensing for hyperspectral image
Author :
Rongqiang Zhao; Qiang Wang; Yi Shen
Author_Institution :
Department of Control Science and Engineering, Harbin Institute of Technology, China, 150001
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 :
"Image reconstruction","Iterative methods","Tensile stress","Compressed sensing","Hyperspectral imaging","Computational modeling"
Conference_Titel :
Communications and Networking in China (ChinaCom), 2015 10th International Conference on
DOI :
10.1109/CHINACOM.2015.7497984