A fast fixed-point algorithm for independent component analysis
Neural Computation
Source separation in astrophysical maps using independent factor analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
On-line non-stationary ICA using mixture models
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Monte Carlo Statistical Methods
Monte Carlo Statistical Methods
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Astrophysical image separation by blind time--frequency source separation methods
Digital Signal Processing
Bayesian separation of images modeled with MRFs using MCMC
IEEE Transactions on Image Processing
Ant estimator with application to target tracking
Signal Processing
An ant stochastic decision based particle filter and its convergence
Signal Processing
Non-stationary t-distribution prior for image source separation from blurred observations
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Cell automatic tracking technique with particle filter
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
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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.