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Hyperspectral super-resolution with coupled Tucker approximation: Recoverability and SVD-based algorithms

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 two SVD-based algorithms that are simple and fast, but with a performance comparable to the state-of-the-art methods. The approach is applicable to the case of unknown spatial degradation and to the pansharpening problem.
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https://hal.archives-ouvertes.fr/hal-01911969
Contributor : Konstantin Usevich Connect in order to contact the contributor
Submitted on : Friday, January 10, 2020 - 11:26:33 AM
Last modification on : Wednesday, November 3, 2021 - 6:02:25 AM
Long-term archiving on: : Saturday, April 11, 2020 - 5:51:26 PM

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Clémence Prévost, Konstantin Usevich, Pierre Comon, David Brie. Hyperspectral super-resolution with coupled Tucker approximation: Recoverability and SVD-based algorithms. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2020, 68, pp.931-946. ⟨10.1109/TSP.2020.2965305⟩. ⟨hal-01911969v4⟩

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