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 for monochromatic images, by a regularisation term, that 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 to a marginal approach on synthetic and real data. © 2014 Springer International Publishing.
CITATION STYLE
Deger, F., Mansouri, A., Pedersen, M., Hardeberg, J. Y., & Voisin, Y. (2014). A variational approach for denoising hyperspectral images corrupted by poisson distributed noise. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8509 LNCS, pp. 106–114). Springer Verlag. https://doi.org/10.1007/978-3-319-07998-1_13
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