Separation of noisy astrophysical images by blind time-frequency source separation methods

Abstract

Two blind time-frequency source separation methods in the literature are adapted to astrophysical image mixtures and four algorithms are developed to separate them into their cosmic components; cosmic microwave background (CMB) radiation, galactic dust and synchrotron. These components simulated according to their physical models are mixed via realistic coefficients, and are subjected to simulated additive, nonstationary Gaussian noise components of realistic power levels, to yield image mixtures. The developed algorithms are compared with the FastICA algorithm and CMB component is found to be recovered with an improvement reaching to 3.16 decibels from CMB-synchrotron mixtures.