The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Image Noise Reduction Filter Based on Robust Regression Model

로버스트 회귀모형에 근거한 영상 잡음 제거 필터

  • Kim, Yeong-Hwa (Department of Applied Statistics, Chung-Ang University) ;
  • Park, Youngho (Department of Statistics, Graduate School of Chung-Ang University)
  • 김영화 (중앙대학교 응용통계학과) ;
  • 박영호 (중앙대학교 대학원 통계학과)
  • Received : 2015.08.10
  • Accepted : 2015.09.16
  • Published : 2015.10.31
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Abstract

Digital images acquired by digital devices are used in many fields. Applying statistical methods to the processing of images will increase speed and efficiency. Methods to remove noise and image quality have been researched as a basic operation of image processing. This paper proposes a novel reduction method that considers the direction and magnitude of the edge to remove image noise effectively using statistical methods. The proposed method estimates the brightness of pixels relative to pixels in the same direction based on a robust regression model. An estimate of pixel brightness is obtained by weighting the magnitude of the edge that improves the performance of the average filter. As a result of the simulation study, the proposed method retains pixels that are well-characterized and confirms that noise reduction performance is improved over conventional methods.

영상은 렌즈를 통하여 형성된 이미지로 많은 응용 분야에서 사용된다. 디지털 기기로 획득한 디지털 영상은 수치화된 자료로 통계분석이 가능하며, 신속하고 효율적인 작업이 가능하게 한다. 영상처리 분야에서는 화질의 개선을 위해서 잡음을 제거하는 방법들이 연구되고 있다. 본 논문은 영상 잡음을 효과적으로 제거하는 방법으로 통계적 방법들을 사용하여, 에지의 방향과 크기를 적용한 새로운 잡음 제거 방법을 제안한다. 이 방법은 동일한 방향에 위치한 화소들에 대하여 로버스트 회귀모형을 적용하고 해당 화소의 밝기 값을 추정한다. 추정된 화소의 밝기 값은 에지의 크기가 가중값으로 사용되어 평균필터의 성능을 개선한다. 모의실험의 결과, 제안한 방법은 특징을 포함하는 화소를 잘 유지하며, 잡음 제거 성능도 기존의 방법보다 개선되는 것을 확인하였다.

Keywords

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