Low-Rank Tensor Tucker Decomposition for Hyperspectral Images Super-Resolution

Super-resolution is an important way to improve the spatial resolution of Hyperspectral images (HSIs). In this paper, we propose a super-resolution method based on low-rank tensor Tucker Decomposition and weighted 3D total variation (TV) for HSIs. Global tensor Tucker decomposition and weighted 3D TV regularization are combined to exploit the prior knowledge of data low-rank information and local smoothness. Meanwhile, we use log-sum norm for tensor Tucker Decomposition to approximate the low-rank tensor. Extensive experiments show that our method outperforms some state-of-the-art methods on public HSI dataset.