Coupled Tensor Low-rank Multilinear Approximation for Hyperspectral Super-resolution

Clémence Prévost; Konstantin Usevich; Pierre Comon; David Brie

ICASSP 2019accepted0 citations

Coupled Tensor Low-rank Multilinear Approximation for Hyperspectral Super-resolution

Clémence Prévost, Konstantin Usevich, Pierre Comon, David Brie

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 an SVD-based algorithm that is simple and fast, but with a performance comparable to that of the state-of-the-art methods.

BibTeX

@inproceedings{icassp2019_coupledtensorlow,
  title = {Coupled Tensor Low-rank Multilinear Approximation for Hyperspectral Super-resolution},
  author = {Clémence Prévost and Konstantin Usevich and Pierre Comon and David Brie},
  booktitle = {ICASSP 2019},
  year = {2019}
}