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Article Dans Une Revue IEEE Transactions on Signal Processing Année : 2020

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

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 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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Dates et versions

hal-01911969 , version 1 (05-11-2018)
hal-01911969 , version 2 (19-11-2018)
hal-01911969 , version 3 (11-04-2019)
hal-01911969 , version 4 (10-01-2020)

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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, 2020, 68, pp.931-946. ⟨10.1109/TSP.2020.2965305⟩. ⟨hal-01911969v4⟩
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