Hyperspectral super-resolution accounting for spectral variability: coupled tensor LL1-based recovery and blind unmixing of the unknown super-resolution image. (English) Zbl 1524.68425
Summary: In this paper, we propose to jointly solve the hyperspectral super-resolution problem and the unmixing problem of the underlying super-resolution image using a coupled LL1 block-tensor decomposition. We consider a spectral variability phenomenon occurring between the observed low-resolution images. Exact recovery conditions for the image and mixing factors are provided. We propose two algorithms, an unconstrained one and another one subject to nonnegativity constraints, to solve the problems at hand. We showcase performance of the proposed approach on synthetic and real images.
MSC:
| 68U10 | Computing methodologies for image processing |
Keywords:
hyperspectral super-resolution; spectral variability; hyperspectral unmixing; image fusion; tensor decompositionsSoftware:
TensorlabReferences:
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