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

Abstract : 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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https://hal.archives-ouvertes.fr/hal-03158076
Contributor : Clémence Prévost Connect in order to contact the contributor
Submitted on : Tuesday, November 2, 2021 - 11:27:13 AM
Last modification on : Tuesday, January 4, 2022 - 6:09:15 AM

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  • HAL Id : hal-03158076, version 2

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Clémence Prévost, Ricardo 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, Society for Industrial and Applied Mathematics, In press. ⟨hal-03158076v2⟩

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