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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HSR_TSP.pdf (2 Mo)
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exp3_table2.txt (639 B)
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exp3_table2_sal.txt (563 B)
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table_BCuprite.txt (596 B)
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table_BPavia.txt (688 B)
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