Astrophysical image denoising using bivariate isotropic Cauchy distributions in the undecimated wavelet domain (TA-L2)

Author(s) : Alin Achim (Istituto di Scienza e Tecnologie dell'Informazione, Italy) Diego Herranz (Istituto di Scienza e Tecnologie dell'Informazione, Italy) Ercan Kuruoglu (Istituto di Scienza e Tecnologie dell'Informazione, Italy)

Abstract : Within the framework of wavelet analysis, we describe a novel technique for removing noise from astrophysical images. We design a Bayesian estimator, which relies on a particular member of the family of isotropic alpha-stable distributions, namely the bivariate Cauchy density. Using the bivariate Cauchy model we develop a noise-removal processor that takes into account the interscale dependencies of wavelet coefficients. We show through simulations that our proposed technique outperforms existing methods both visually and in terms of root mean squared error.

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