  In this paper, we propose a novel approach to hyperspectral image
super-resolution by modeling the global spatial-and-spectral correlation and
local smoothness properties over hyperspectral images. Specifically, we utilize
the tensor nuclear norm and tensor folded-concave penalty functions to describe
the global spatial-and-spectral correlation hidden in hyperspectral images, and
3D total variation (TV) to characterize the local spatial-and-spectral
smoothness across all hyperspectral bands. Then, we develop an efficient
algorithm for solving the resulting optimization problem by combing the local
linear approximation (LLA) strategy and alternative direction method of
multipliers (ADMM). Experimental results on one hyperspectral image dataset
illustrate the merits of the proposed approach.
