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Noise Removal Using Complex Wavelet and Bernoulli-Gaussian Model  

Eom Il-Kyu (Dept. of Electronics Engineering, Pusan National University)
Kim Yoo-Shin (Dept. of Electronics Engineering, Pusan National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.43, no.5, 2006 , pp. 52-61 More about this Journal
Abstract
Orthogonal wavelet tansform which is generally used in image and signal processing applications has limited performance because of lack of shift invariance and low directional selectivity. To overcome these demerits complex wavelet transform has been proposed. In this paper, we present an efficient image denoising method using dual-tree complex wavelet transform and Bernoulli-Gauss prior model. In estimating hyper-parameters for Bernoulli-Gaussian model, we present two simple and non-iterative methods. We use hypothesis-testing technique in order to estimate the mixing parameter, Bernoulli random variable. Based on the estimated mixing parameter, variance for clean signal is obtained by using maximum generalized marginal likelihood (MGML) estimator. We simulate our denoising method using dual-tree complex wavelet and compare our algorithm to well blown denoising schemes. Experimental results show that the proposed method can generate good denoising results for high frequency image with low computational cost.
Keywords
복소수 웨이블릿;베이즈 추정법;잡음 제거;베르누이-가우스 모델;가설-검증 문제;
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