Hyperspectral super-resolution accounting for spectral variability: coupled tensor LL1-based recovery and blind unmixing of the unknown super-resolution image - ANR - Agence nationale de la recherche Accéder directement au contenu
Article Dans Une Revue SIAM Journal on Imaging Sciences Année : 2022

Hyperspectral super-resolution accounting for spectral variability: coupled tensor LL1-based recovery and blind unmixing of the unknown super-resolution image

Résumé

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 non-negativity constraints, to solve the problems at hand. We showcase performance of the proposed approach on synthetic and real images.
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Dates et versions

hal-03158076 , version 1 (03-03-2021)
hal-03158076 , version 2 (02-11-2021)

Identifiants

Citer

Clémence Prévost, Ricardo A Borsoi, Konstantin Usevich, David Brie, José C. M. Bermudez, et al.. Hyperspectral super-resolution accounting for spectral variability: coupled tensor LL1-based recovery and blind unmixing of the unknown super-resolution image. SIAM Journal on Imaging Sciences, 2022, 15 (1), pp.110-138. ⟨10.1137/21M1409354⟩. ⟨hal-03158076v2⟩
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