Pattern recognition (psychology)

Image (mathematics)

Hyperspectral imaging

Mathematics

Computer science

Astronomy

Rank (graph theory)

Computer vision

Algorithm

Resolution (logic)

Spectral line

Combinatorics

Superresolution

Artificial intelligence

Multispectral image

Geometry

Spectral resolution

Physics

Tensor (intrinsic definition)

Image resolution

IEEE Transactions on Neural Networks and Learning Systems

Hyperspectral images (HSIs) with high spectral resolution only have the low spatial resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution can be obtained with higher spatial resolution. Therefore, fusing the high-spatial-resolution MSI (HR-MSI) with low-spatial-resolution HSI of the same scene has become the very popular HSI super-resolution scheme. In this paper, a novel low tensor-train (TT) rank (LTTR)-based HSI super-resolution method is proposed, where an LTTR prior is designed to learn the correlations among the spatial, spectral, and nonlocal modes of the nonlocal similar high-spatial-resolution HSI (HR-HSI) cubes. First, we cluster the HR-MSI cubes as many groups based on their similarities, and the HR-HSI cubes are also clustered according to the learned cluster structure in the HR-MSI cubes. The HR-HSI cubes in each group are much similar to each other and can constitute a 4-D tensor, whose four modes are highly correlated. Therefore, we impose the LTTR constra...

Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution