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

The Institute of Electronics and Information Engineers (대한전자공학회)
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A Study on Nonlinear Noise Removal for Images Corrupted with ${\alpha}$-Stable Random Noise

${\alpha}$-stable 랜덤잡음에 노출된 이미지에 적용하기 위한 비선형 잡음제거 알고리즘에 관한 연구

  • Hahn, Hee-Il (Department of Information and Communications Engineering, Hankuk University of Foreign Studies)
  • 한희일 (한국외국어대학교 정보통신공학과)
  • Published : 2007.11.25
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Abstract

Robust nonlinear image denoising algorithms for the class of ${\alpha}$-stable distribution are introduced. The proposed amplitude-limited sample average filter(ALSAF) proves to be the maximum likelihood estimator under the heavy-tailed Gaussian noise environments. The error norm for this estimator is equivalent to Huber#s minimax norm. It is optimal in the respect of maximizing the efficacy under the above noise environment. It is mired with the myriad filter to propose an amplitude-limited myriad filter(ALMF). The behavior and performance of the ALSAF and ALMF in ${\alpha}$-stable noise environment are illustrated and analyzed through simulation.

본 논문에서는 ${\alpha}$-stable 확률분포를 갖는 잡음에 열화된 이미지의 화질을 개선하는 알고리즘을 제안한다. 제안한 진폭제한 평균필터(amplitude-limited sample average filter)는 heavy-tailed 가우시안 잡음환경 하에서 maximum likelihood estimator (MLE)임을 증명한다. 그리고, 이 알고리즘에 해당하는 error norm은 Huber의 minimax norm과 일치하고, 위에서 언급한 잡음 환경 하에서 efficacy를 최대화한다는 점에서 최적의 필터임을 보인다. 이 개념을 미리어드(myriad) 필터와 결합하여 진폭제한 미리어드 필터(amplitude-limited myriad filter)를 제안하고 실험을 통하여 이의 성능을 확인한다.

Keywords

References

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