The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Noise reduction algorithm for an image using nonparametric Bayesian method

비모수 베이지안 방법을 이용한 영상 잡음 제거 알고리즘

  • Woo, Ho-young (Department of Applied Statistics, Chung-Ang University) ;
  • Kim, Yeong-hwa (Department of Applied Statistics, Chung-Ang University)
  • 우호영 (중앙대학교 응용통계학과) ;
  • 김영화 (중앙대학교 응용통계학과)
  • Received : 2018.04.09
  • Accepted : 2018.08.01
  • Published : 2018.10.31
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Abstract

Noise reduction processes that reduce or eliminate noise (caused by a variety of reasons) in noise contaminated image is an important theme in image processing fields. Many studies are being conducted on noise removal processes due to the importance of distinguishing between noise added to a pure image and the unique characteristics of original images. Adaptive filter and sigma filter are typical noise reduction filters used to reduce or eliminate noise; however, their effectiveness is affected by accurate noise estimation. This study generates a distribution of noise contaminating image based on a Dirichlet normal mixture model and presents a Bayesian approach to distinguish the characteristics of an image against the noise. In particular, to distinguish the distribution of noise from the distribution of characteristics, we suggest algorithms to develop a Bayesian inference and remove noise included in an image.

영상처리 분야의 중요한 주제인 영상의 잡음 제거 과정은 원래의 순수한 영상이 다양한 원인으로 발생한 잡음에 의해 오염되었을때 이 잡음을 제거하거나 줄이는 것을 의미한다. 잡음 제거 과정에서는 영상에 추가된 잡음과 원 영상이 가진 고유한 특징들을 구별해내는 것이 중요하며 이에 대한 많은 연구가 진행되고 있다. 적응적 필터와 시그마 필터는 잡음 제거를 위하여 사용하는 대표적인 잡음 제거 필터이며 이 필터들의 효용성은 정확한 잡음 추정에 영향을 받는다. 따라서 본 연구에서는 디리클레 정규 혼합모형을 토대로 영상을 오염시키고 있는 잡음의 분포를 생성하고 이를 토대로 영상의 특징과 잡음을 구별하기 위한 베이지안 방법을 제시한다. 특히 잡음의 분포와 특징의 분포를 구별하기 위해 베이지안 추론을 전개하고 영상에 포함된 잡음을 제거하는 알고리즘을 제시하고자 한다.

Keywords

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