Image separation using particle filters

Costagli M., Kuruoglu E. E.

PLANCK satellite mission  Bayesian source separation  Artificial Intelligence  Independent component analysis  Computer Vision and Pattern Recognition  Statistics  Signal Processing  Cosmic microwave background radiation  Computational Theory and Mathematics  Electrical and Electronic Engineering  Sequential Monte Carlo  Particle filtering  Applied Mathematics  Probability and Uncertainty 

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.

Source: Digital signal processing (Print) 17 (2007): 935–946. doi:10.1016/j.dsp.2007.04.003

Publisher: Academic Press,, Duluth, MN , Stati Uniti d'America

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