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Signal, Image and Video Processing

, Volume 10, Issue 3, pp 447–454 | Cite as

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

  • Alamin MansouriEmail author
  • Ferdinand Deger
  • Marius Pedersen
  • Jon Y. Hardeberg
  • Yvon Voisin
Original Paper

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.

Keywords

Adaptive total variation  Hyperspectral images Poisson noise 

Notes

Acknowledgments

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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Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Alamin Mansouri
    • 1
    Email author
  • Ferdinand Deger
    • 1
    • 2
  • Marius Pedersen
    • 2
  • Jon Y. Hardeberg
    • 2
  • Yvon Voisin
    • 1
  1. 1.Laboratoire LE2IAuxerreFrance
  2. 2.The Norwegian Colour and Visual Computing Laboratory, Faculty of Computer Science and Media TechnologyGjovik University CollegeGjovikNorway

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