Abstract
We recently proposed a markovian image separation method. The proposed algorithm is however very time consuming so that it cannot be applied to large-size real-world images. In this paper, we propose two major modifications i.e. utilization of a low-cost parametric score function estimator and derivation of a modified equivariant version of Newton-Raphson algorithm for solving the estimating equations. These modifications make the algorithm much faster and allow us to perform more experiments with artificial and real data which are presented in the paper.
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Keywords
- Score Function
- Markov Random Field
- Independent Component Analysis
- Source Separation
- Blind Source Separation
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References
Hosseini, S., Jutten, C., Pham, D.-T.: Markovian source separation. IEEE Trans. on Signal Processing 51, 3009–3019 (2003)
Hosseini, S., Guidara, R., Deville, Y., Jutten, C.: Maximum likelihood separation of spatially autocorrelated images using a Markov model. In: Proc. of MAXENT 2005, San Jose, USA (August 2005)
Kuruoglu, E.E., Tonazzini, A., Bianchi, L.: Source separation in noisy astrophysical images modelled by markov random fields. In: Proc. ICIP 2004, pp. 2701–2704 (2004)
Pham, D.-T., Garat, P.: Blind separation of mixture of independent sources through a quasi-maximum likelihood approach. IEEE Trans. on Signal Processing 45, 1712–1725 (July 1997)
Tong, L., Liu, R., Soon, V., Huang, Y.: Indeterminacy and identifiability of blind identification. IEEE Trans. on Circuits Syst. 38, 499–509 (1991)
Belouchrani, A., Abed Meraim, K., Cardoso, J.-F., Moulines, E.: A blind source separation technique based on second order statistics. IEEE Trans. on Signal Processing 45, 434–444 (1997)
Pham, D.-T.: Fast algorithms for mutual information based independent component analysis. IEEE Trans. on Signal Processing 52(10) (October 2004)
Pham, D.-T., Garat, P., Jutten, C.: Separation of mixture of independent sources through a quasi-maximum likelihood approach. In: Proc. of EUSIPCO 1992, Brussels, August 1992, pp. 771–774 (1992)
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Hosseini, S., Guidara, R., Deville, Y., Jutten, C. (2006). Markovian Blind Image Separation. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_14
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DOI: https://doi.org/10.1007/11679363_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32630-4
Online ISBN: 978-3-540-32631-1
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