Non-stationary t-distribution prior for image source separation from blurred observations (Contributo in atti di convegno)

Type
Label
  • Non-stationary t-distribution prior for image source separation from blurred observations (Contributo in atti di convegno) (literal)
Anno
  • 2010-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-642-15995-4_63 (literal)
Alternative label
  • Kayabol K., Kuruoglu E. E. (2010)
    Non-stationary t-distribution prior for image source separation from blurred observations
    in LVA/ICA 2010 - Latent Variable Analysis and Signal Separation. 9th International Conference, St. Malo, France, 27-30 September 2010
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Kayabol K., Kuruoglu E. E. (literal)
Pagina inizio
  • 506 (literal)
Pagina fine
  • 513 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.springerlink.com/content/r85885j6t37n3642/ (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 6365 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 6365 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • In: LVA/ICA 2010 - Latent Variable Analysis and Signal Separation. 9th International Conference (St. Malo, France, 27-30 September 2010). Proceedings, pp. 506 - 513. Vincent Vigneron, Vicente Zarzoso, Eric Moreau, Rémi Gribonval, Emmanuel Vincent (eds.). (Lecture Notes in Computer Science, vol. 6365). Springer, 2010. (literal)
Note
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
  • PuMa (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy (literal)
Titolo
  • Non-stationary t-distribution prior for image source separation from blurred observations (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-3-642-15995-4 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Vincent Vigneron, Vicente Zarzoso, Eric Moreau, Rémi Gribonval, Emmanuel Vincent (literal)
Abstract
  • We propose a non-stationary spatial image model for blind image separation problem. Our model is defined on first order image differentials. We model the image differentials using t-distribution with space varying scale parameters. This prior image model has been used in the Bayesian formulation and the image source are estimated using a Langevin sampler method. We have tested the proposed model on astrophysical image mixtures and obtained better results regarding to stationary model. (literal)
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