Hyperspectral super-resolution accounting for spectral variability: LL1-based recovery and blind unmixing

In this paper, we propose to jointly solve the hyperspectral super-resolution and hyperspectral unmixing problems using a coupled LL1 block-tensor decomposition. We focus on the specific case of spectral variability occurring between the observed low-resolution images. Exact recovery conditions are provided. We propose two algorithms: an unconstrained one and another one subject to non-negativity constraints, to solve the problems at hand. We showcase performance of the proposed approach on a set of synthetic and semi-real images.