Astrophysical image separation by blind time-frequency source separation methods

Ozgen M. T., Kuruoglu E. E., Herranz D.

Source separation  Artificial Intelligence  Blind Time-Frequency Source Separation Methods  Computer Vision and Pattern Recognition  Astrophysical image separation  J.2 Physical Sciences and Engineering. Astronomy  42B99 Fourier analysis in several variables  Statistics  Joint Diagonalization  Signal Processing  Cosmic microwave background radiation  Blind time-frequency source separation  Computational Theory and Mathematics  Source Separation  Electrical and Electronic Engineering  Cosmic Microwave Background Radiation  85A35 Statistical astronomy  Astrophysical Image Separation  Applied Mathematics  I.4.4 Restoration  Joint diagonalization  Probability and Uncertainty 

In this paper, two prevalent blind time-frequency (TF) source separation methods in the literature are adapted to astrophysical image mixtures and four algorithms are developed to separate them into their astrophysical components. The components considered in this work are cosmic microwave background (CMB) radiation, galactic dust and synchrotron, among which the CMB component is emphasized. These simulated components mixed via realistic coefficients are subjected to simulated additive, nonstationary Gaussian noise components of realistic power levels, to yield image mixtures on which our orthogonal and nonorthogonal TF algorithms are applied. The developed algorithms are compared with the FastICA algorithm and CMB component is found to be recovered with an improvement reaching to 3.25 decibels from CMB-synchrotron mixtures. The proposed techniques are believed to be generically applicable in separating other types of astrophysical components as well.

Source: Digital signal processing (Print) 19 (2009): 360–369. doi:10.1016/j.dsp.2007.12.003

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

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