Astrophysical image denoising using bivariate isotropic Cauchy distributions in the undecimated wavelet domain

Details Astrophysical image denoising using bivariate isotropic Cauchy distributions in the undecimated wavelet domain Astrophysical image denoising using bivariate isotropic Cauchy distributions in the undecimated wavelet domain

Apr 2005

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005impr.conf.1225a&link_type=abstract

2004 International Conference on Image Processing, Vol. 2, p. 1225-1228

Astronomy and Astrophysics

Astronomy

1

Astronomical Techniques, Astronomy Computing, Image Denoising, Image Enhancement, Mean Square Error Methods, Wavelet Transforms

Scientific paper

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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