Fast holo-kronecker compressive sensing for hyperspectral image

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.