  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.
