Coupled Tensor Low-rank Multilinear Approximation for Hyperspectral Super-resolution

We propose a novel approach for hyperspectral super-resolution that is based on low-rank tensor approximation for a coupled low-rank multilinear (Tucker) model. We show that the correct recovery holds for a wide range of multilinear ranks. For coupled tensor approximation, we propose an SVD-based al...

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Bibliographic Details
Main Authors: Prevost, C., Usevich, K., Comon, P., Brie, D.
Format: Conference Proceeding
Language:English
Subjects:
data fusion
hyperspectral super-resolution
identifiability
low-rank tensor factorizations
recovery
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Summary:We propose a novel approach for hyperspectral super-resolution that is based on low-rank tensor approximation for a coupled low-rank multilinear (Tucker) model. We show that the correct recovery holds for a wide range of multilinear ranks. For coupled tensor approximation, we propose an SVD-based algorithm that is simple and fast, but with a performance comparable to that of the state-of-the-art methods.
ISSN:2379-190X
DOI:10.1109/ICASSP.2019.8683619