High-Order Coupled Fully-Connected Tensor Network Decomposition for Hyperspectral Image Super-Resolution

TL;DR

This paper introduces a novel high-order coupled fully-connected tensor network decomposition method for hyperspectral image super-resolution, effectively fusing low-resolution hyperspectral and high-resolution multispectral images to produce high-resolution hyperspectral images.

Contribution

The paper proposes a new tensor network decomposition approach that captures intrinsic correlations between tensor modes, improving hyperspectral image super-resolution performance.

Findings

Outperforms existing super-resolution methods on three datasets.

Effectively preserves spectral information with weighted-graph regularization.

Demonstrates superior ability to model high-order tensor correlations.

Abstract

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.