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Image Source Separation Using Color Channel Dependencies

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Independent Component Analysis and Signal Separation (ICA 2009)

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

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Abstract

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.

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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References

  1. Bronstein, A.M., Bronstein, M.M., Zibulevsky, M., Zeevi, Y.Y.: Sparse ICA for blind separation of transmitted and reflected images. Int. J. Imaging Science Technology 15, 84–91 (2005)

    Article  Google Scholar 

  2. Bedini, L., Herranz, D., Salerno, E., Baccigalupi, C., Kuruoglu, E.E., Tonazzini, A.: Separation of correlated astrophysical sources using multiple-lag data covariance matrices. EURASIP Journal on Applied Signal Processing 15, 2400–2412 (2005)

    Article  MATH  Google Scholar 

  3. Caiafa, C.F., Kuruoglu, E.E., Proto, A.N.: A minimax entropy method for blind separation of dependent components in astrophysical images. In: AIP-Proceedings of MaxEnt 2006, pp. 81–88 (2006)

    Google Scholar 

  4. Gencaga, D., Kuruoglu, E.E., Ertuzun, A.: Bayesian separation of non-stationary mixtures of dependent Gaussian sources. In: AIP-Proceedings of MaxEnt 2005 (2005)

    Google Scholar 

  5. Hyvarinen, A., Hurri, J.: Blind separation of sources that have spatiotemporal variance dependenies. Signal Process. 84, 247–254 (2004)

    Article  MATH  Google Scholar 

  6. Kawanabe, M., Muller, K.-R.: Estimating functions for blind separation when sources have variance dependenies. Journal Machine Learning 6, 453–482 (2005)

    MATH  Google Scholar 

  7. Zhang, K., Chan, L.-W.: An adaptive method for subband decomposition ICA. Neural Computation 18, 191–223 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  8. Abrard, F., Deville, Y.: A time-frequency blind signal separation method applicable to underdetermined mixtures of dependent sources. Signal Process. 85, 1389–1403 (2005)

    Article  MATH  Google Scholar 

  9. Gilks, W.R., Richardson, S., Spiegalhalter, D.J.: Markov Chain Monte Carlo in Practice. Chapman & Hall, London (1996)

    MATH  Google Scholar 

  10. Kayabol, K., Kuruoglu, E.E., Sankur, B.: Bayesian separation of images modelled with MRFs using MCMC. IEEE Trans. Image Process. (accepted, 2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. ISTI, CNR, via G. Moruzzi 1, 56124, Pisa, Italy

    Koray Kayabol & Ercan E. Kuruoglu

  2. Bogazici University, Bebek, 80815, Istanbul, Turkey

    Bulent Sankur

Authors
  1. Koray Kayabol
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  2. Ercan E. Kuruoglu
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  3. Bulent Sankur
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Electrical Engineering, ITE 324, University of Maryland, Baltimore County, 1000 Hilltop Circle, MD 21250, Baltimore, USA

    Tülay Adali

  2. Domaine Universitaire, GIPSA-lab, BP 46, 38402, Saint Martin d’Hères Cedex, France

    Christian Jutten

  3. Departamento de Microonda e Óptica (DMO), FEEC / Unicamp, Avenida Albert Einstein 400, 13083-852, Campinas, Sao Paulo, Brazil

    João Marcos Travassos Romano

  4. Centro Tecnológico, Curso de Engenharia Elétrica, Universidade Federal do Maranhão, Avenida dos Portugueses, s/n, Bacanga, 65080-040, São Luís, MA, Brazil

    Allan Kardec Barros

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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. https://doi.org/10.1007/978-3-642-00599-2_63

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  • DOI: https://doi.org/10.1007/978-3-642-00599-2_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00598-5

  • Online ISBN: 978-3-642-00599-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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