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


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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We would like to warmly thank the Regional Council of Burgundy for supporting this work. Support agreement FEDER 2011-9201AAO048S04661. The dataset of Kremer pigment chart was gratefully provided by Norsk Elektro Optikk AS.

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Correspondence to Alamin Mansouri.

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Mansouri, A., Deger, F., Pedersen, M. et al. An adaptive spatial–spectral total variation approach for Poisson noise removal in hyperspectral images. SIViP 10, 447–454 (2016).

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  • Adaptive total variation
  • Hyperspectral images
  • Poisson noise