Coupled tensor low-rank multilinear approximation for hyperspectral super-resolution

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-02025385
Contributor : Konstantin Usevich <>
Submitted on : Tuesday, February 19, 2019 - 4:16:14 PM
Last modification on : Saturday, April 13, 2019 - 8:55:55 AM

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Clémence Prévost, Konstantin Usevich, Pierre Comon, David Brie. Coupled tensor low-rank multilinear approximation for hyperspectral super-resolution. 44th IEEE International Conference on Acoustics Speech and Signal Processing, ICASSP 2019, May 2019, Brighton, United Kingdom. ⟨hal-02025385⟩

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