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Article Dans Une Revue Signal, Image and Video Processing Année : 2015

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

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Résumé

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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Dates et versions

hal-01257583 , version 1 (17-01-2016)

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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, 2015, ⟨10.1007/s11760-015-0806-0⟩. ⟨hal-01257583⟩
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