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Communication Dans Un Congrès Année : 2019

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

Résumé

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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Dates et versions

hal-02025385 , version 1 (19-02-2019)

Identifiants

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Clémence Prévost, Konstantin Usevich, Pierre Comon, David Brie. Coupled tensor low-rank multilinear approximation for hyperspectral super-resolution. ICASSP 2019 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2019, Brighton, United Kingdom. ⟨10.1109/ICASSP.2019.8683619⟩. ⟨hal-02025385⟩
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