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Image Source Separation Using Color Channel Dependencies

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 5441)


We investigate the problem of source separation in images in the Bayesian framework using the color channel dependencies. As a case in point we consider the source separation of color images which have dependence between its components. A Markov Random Field (MRF) is used for modeling of the inter and intra-source local correlations. We resort to Gibbs sampling algorithm for obtaining the MAP estimate of the sources since non-Gaussian priors are adopted. We test the performance of the proposed method both on synthetic color texture mixtures and a realistic color scene captured with a spurious reflection.


  • Color Image
  • Markov Random Field
  • Source Separation
  • Gibbs Distribution
  • Color Texture

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This work was supported by CNR-TUBITAK joint project No. 104E101. Partial support has also been given by the Italian Space Agency (ASI), under project COFIS.

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© 2009 Springer-Verlag Berlin Heidelberg

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Kayabol, K., Kuruoglu, E.E., Sankur, B. (2009). Image Source Separation Using Color Channel Dependencies. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00598-5

  • Online ISBN: 978-3-642-00599-2

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