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Article dans une revue

An adaptive spatial–spectral total variation approach for Poisson noise removal in hyperspectral images

Abstract : Poisson distributed noise, such as photon noise, is an important noise source in multi- and hyperspectral images. We propose a variational-based denoising approach that accounts the vectorial structure of a spectral image cube, as well as the Poisson distributed noise. For this aim, we extend an approach initially developed for monochromatic images, by a regularisation term, which is spectrally and spatially adaptive and preserves edges. In order to take the high computational complexity into account, we derive a split Bregman optimisation for the proposed model. The results show the advantages of the proposed approach compared with a marginal approach on synthetic and real data.
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01257583
Contributeur : Alamin Mansouri <>
Soumis le : dimanche 17 janvier 2016 - 20:37:49
Dernière modification le : vendredi 17 juillet 2020 - 14:54:06

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Alamin Mansouri, Ferdinand Deger, Marius Pedersen, Jon Yngve Hardeberg, Yvon Voisin. An adaptive spatial–spectral total variation approach for Poisson noise removal in hyperspectral images. Signal, Image and Video Processing, Springer Verlag, 2015, ⟨10.1007/s11760-015-0806-0⟩. ⟨hal-01257583⟩

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