Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Effective Parameter Estimation of Bernoulli-Gaussian Mixture Model and its Application to Image Denoising

베르누이-가우스 혼합 모델의 효과적인 파라메터 추정과 영상 잡음 제거에 응용

  • Eom, Il-Kyu (Dept. of Information and Communications, Miryang National University) ;
  • Kim, Yoo-Shin (Research Institute of Computer and Information and Communictaion)
  • 엄일규 (밀양대학교 정보통신공학과) ;
  • 김유신 (부산대학교 컴퓨터 및 정보통신 연구소)
  • Published : 2005.09.25
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Abstract

In general, wavelet coefficients are composed of a few large coefficients and a lot of small coefficients. In this paper, we propose image denoising algorithm using Bernoulli-Gaussian mixture model based on sparse characteristic of wavelet coefficient. The Bernoulli-Gaussian mixture is composed of the multiplication of Bernoulli random variable and Gaussian mixture random variable. The image denoising is performed by using Bayesian estimation. We present an effective denoising method through simplified parameter estimation for Bernoulli random variable using local expected squared error. Simulation results show our method outperforms the states-of-art denoising methods when using orthogonal wavelets.

일반적으로 웨이블릿 계수는 적은 수의 크기가 큰 계수와 많은 수의 작은 크기의 계수로 구성되어 있다. 따라서 본 논문에서는 웨이블릿 계수의 성긴 특성에 근거한 베르누이-가우스 혼합 모델을 이용한 잡음 제거 방법을 제안한다. 베르누이-가우스 혼합 모델은 베르누이 랜덤 변수와 가우스 혼합 랜덤 변수의 곱으로 구성되며, 이에 대한 베이지안 추정법으로 잡음 제거를 수행한다. 본 논문에서는 국부 자승 오차의 기대값를 이용하여 통한 베르누이 랜덤 변수에 대한 간략화된 파라메터의 추정을 통하여 효율적인 잡음 제거 방법을 제시한다. 모의실험 결과를 통하여 본 논문의 방법이 직교 웨이블릿 변환을 사용한 최신의 잡음 제거 방법보다 우수한 성능을 나타낸다는 것을 보여준다.

Keywords

References

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