  Hyperspectral image super-resolution addresses the problem of fusing a
low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral
image (HR-MSI) to produce a high-resolution hyperspectral image (HR-HSI).
Tensor analysis has been proven to be an efficient method for hyperspectral
image processing. However, the existing tensor-based methods of hyperspectral
image super-resolution like the tensor train and tensor ring decomposition only
establish an operation between adjacent two factors and are highly sensitive to
the permutation of tensor modes, leading to an inadequate and inflexible
representation. In this paper, we propose a novel method for hyperspectral
image super-resolution by utilizing the specific properties of high-order
tensors in fully-connected tensor network decomposition. The proposed method
first tensorizes the target HR-HSI into a high-order tensor that has multiscale
spatial structures. Then, a coupled fully-connected tensor network
decomposition model is proposed to fuse the corresponding high-order tensors of
LR-HSI and HR-MSI. Moreover, a weighted-graph regularization is imposed on the
spectral core tensors to preserve spectral information. In the proposed model,
the superiorities of the fully-connected tensor network decomposition lie in
the outstanding capability for characterizing adequately the intrinsic
correlations between any two modes of tensors and the essential invariance for
transposition. Experimental results on three data sets show the effectiveness
of the proposed approach as compared to other hyperspectral image
super-resolution methods.
