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Proceedings Paper

Wavelet-based Bayesian denoising using Bernoulli-Gaussian mixture model
Author(s): Il Kyu Eom; Yoo Shin Kim
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Paper Abstract

In general, wavelet coefficients are composed of a few large coefficients and a lot of small ones. Therefore, each wavelet coefficient is efficiently modeled as a random variable of a Bernoulli-Gaussian mixture distribution with unknown parameters. The Bernoulli-Gaussian mixture is composed of the multiplication of the Bernoulli random variable and the Gaussian mixture random variable. In this paper, we propose a denoising algorithm using the Bernoulli-Gaussian mixture model based on sparse characteristics of the wavelet coefficient. The denoising is performed with Bayesian estimation. We present an effective denoising method through simplified parameter estimation for the Bernoulli random variable using a local expected square error. Simulation results showed that our method outperformed the states of the art denoising methods.

Paper Details

Date Published: 24 June 2005
PDF: 9 pages
Proc. SPIE 5960, Visual Communications and Image Processing 2005, 59600Y (24 June 2005); doi: 10.1117/12.631411
Show Author Affiliations
Il Kyu Eom, Miryang National Univ. (South Korea)
Yoo Shin Kim, Pusan National Univ. (South Korea)

Published in SPIE Proceedings Vol. 5960:
Visual Communications and Image Processing 2005
Shipeng Li; Fernando Pereira; Heung-Yeung Shum; Andrew G. Tescher, Editor(s)

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