%0 Journal Article %T An adaptive spatial–spectral total variation approach for Poisson noise removal in hyperspectral images %+ Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i) %+ The Norwegian Colour and Visual Computing Laboratory %A Mansouri, Alamin %A Deger, Ferdinand %A Pedersen, Marius %A Hardeberg, Jon Yngve %A Voisin, Yvon %Z Regional Council of Bourgogne %< avec comité de lecture %@ 1863-1703 %J Signal, Image and Video Processing %I Springer Verlag %8 2015-11-01 %D 2015 %R 10.1007/s11760-015-0806-0 %K Noise Corrupted ImageOriginal Hyperspectral ImageTv DenoisingFixed Pattern NoisePhoton NoiseBregman IterationTv ModelSynthetic ImageRegularisation TermROF ModelPoisson NoiseMonochromatic ImageImage Gradient MagnitudeGaussian Noise ModelNoise-free Image %Z Computer Science [cs]/Signal and Image ProcessingJournal articles %X 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. %G English %L hal-01257583 %U https://u-bourgogne.hal.science/hal-01257583 %~ UNIV-BOURGOGNE %~ CNRS %~ ENSAM %~ LE2I %~ AGREENIUM %~ IMVIA %~ CORES %~ ARTS-ET-METIERS-SCIENCES-ET-TECHNOLOGIES %~ HESAM %~ HESAM-ENSAM %~ INSTITUT-AGRO