Hyperspectral Compressive Sensing Via Spatial-Spectral Total Variation Regularized Low-Rank Tensor Decomposition

Abstract

Hyperspectral compressive sensing (HCS) is considered for reconstructing the hyperspectral image (HSI) from a few random sampled measurements. HCS is crucial for the onboard imaging systems to cut down the acquisition time and data storage volume, and simultaneously maintain image quality. In this paper, a spatial-spectral total variation (SSTV) regularized low-rank tensor decomposition (LRTD) method is proposed for HCS. Specifically, for the HSI, the tensor nuclear norm based LRTD is utilized to characterize the global correlation among all bands, and an anisotropic SSTV regularization is explored to describe the local spatial smooth structure and spectral correlation of adjacent bands. In addition, an efficient algorithm based on the alternative direction multiplier method is developed to solve the resulting optimization problem. Experimental results demonstrate that the proposed method is superior to the existing state-of-the-art ones.