An adaptive spatial–spectral total variation approach for Poisson noise removal in hyperspectral images. Issue 3 (March 2016) - Mansouri, Alamin; Deger, Ferdinand; Pedersen, Marius; Hardeberg, Jon; Voisin, Yvon
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.