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Hyperspectral Super-Resolution With Coupled Tucker Approximation: Recoverability and SVD-Based Algorithms
IEEE Transactions on Signal Processing ( IF 5.230 ) Pub Date : 2020-01-15 , DOI: 10.1109/tsp.2020.2965305
Clémence Prévost; Konstantin Usevich; Pierre Comon; David Brie

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.
更新日期:2020-02-14

 

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