Costagli M., Kuruoglu E. E. Image separation using particle filters. In: Digital Signal Processing, vol. 17 (5) pp. 935 - 946. Bayesian Source Separation. Ercan E. Kuruoglu and Kevin Knuth (eds.). Elsevier, 2007. |

Abstract (English) |
In this work, we will analyze the problem of source separation in the case of superpositions of different source images, which need to be extracted from a set of noisy observations. This problem occurs, for example, in the field of astrophysics, where the contributions of various Galactic and extra-Galactic components need to be separated from a set of observed noisy mixtures. Most of the previous work on the problem performed blind source separation, assuming noiseless models, and in the few cases when noise is taken into account, it is assumed that it is Gaussian and space-invariant. In this paper we review the theoretical fundamentals of particle filtering, an advanced Bayesian estimation method which can deal with non-Gaussian non-linear models and additive space-varying noise, and we introduce a hierarchical model and a fusion of multiple particle filters for the solution of the image separation problem. Our simulations on realistic astrophysical data show that the particle filter approach provides significantly better results in comparison with one of the most widespread algorithms for source separation (FastICA), especially in the case of low SNR. | |

URL: | http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WDJ-4NJ20D1-1&_user=5511047&_coverDate=09%2F30%2F2007&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000061181&_version=1&_urlVersion=0&_userid=5511047&md5=cc36453d2caa10fe663411d5c3602b43 | |

Subject | Sequential Monte Carlo PLANCK satellite mission Particle filtering Bayesian source separation Independent component analysis Cosmic microwave background radiation G.3 Probability and Statistics. Probabilistic algorithms (including Monte Carlo) I.4.5 Reconstruction J.2 Physical Sciences and Engineering. Astronomy 65C35 Stochastic particle methods 85-08 Computational methods 65C05 Monte Carlo methods |

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