Abstract
Hyperspectral image (HSI) super-resolution, which fuses
a low-resolution (LR) HSI with a high-resolution (HR) multispectral image (MSI), has recently attracted much attention. Most of the current HSI super-resolution approaches
are based on matrix factorization, which unfolds the threedimensional HSI as a matrix before processing. In general,
the matrix data representation obtained after the matrix unfolding operation makes it hard to fully exploit the inherent
HSI spatial-spectral structures. In this paper, a novel HSI
super-resolution method based on non-local sparse tensor
factorization (called as the NLSTF) is proposed. The sparse
tensor factorization can directly decompose each cube of
the HSI as a sparse core tensor and dictionaries of three
modes, which reformulates the HSI super-resolution problem as the estimation of sparse core tensor and dictionaries
for each cube. To further exploit the non-local spatial selfsimilarities of the HSI, similar cubes are grouped together,
and they are assumed to share the same dictionaries. The
dictionaries are learned from the LR-HSI and HR-MSI for
each group, and corresponding sparse core tensors are estimated by spare coding on the learned dictionaries for each
cube. Experimental results demonstrate the superiority of
the proposed NLSTF approach over several state-of-the-art
HSI super-resolution approaches.