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영상에 포함된 잡음의 분산 추정과 잡음제거

Estimation of the Noise Variance in Image and Noise Reduction

  • 김영화 (중앙대학교 응용통계학과) ;
  • 남지호 (중앙대학교 대학원 통계학과)
  • Kim, Yeong-Hwa (Department of Applied Statistics, Chung-Ang University) ;
  • Nam, Ji-Ho (Department of Statistics, Graduate School of Chung-Ang University)
  • 투고 : 20110600
  • 심사 : 20110800
  • 발행 : 2011.10.31

초록

영상처리 분야에서 원래의 순수 이미지를 오염시키는 잡음을 제거하는 것은 매우 중요한 문제이다. 그러나 여러 가지 원인으로 인하여 발생하는 잡음의 발생을 완벽하게 막는 것은 현실적으로 불가능하다. 따라서 영상에 포함되어 있는 잡음을 제거하거나 최대한 줄이는 것이 매우 중요한 과제이다. 본 연구에서는 이미지를 오염시키고 있는 잡음의 상대적인 크기를 측정하여 잡음의 분산의 수준을 추정하고, 이를 영상처리 분야에서 자주 사용되는 잡음제거 기법인 시그마 필터에 응용하여 잡음을 효과적으로 제거하는 통계적 알고리즘을 제시하였다. 결론적으로, 잡음의 분산의 수준에 관계없이 본 연구에서 제안한 통계적 잡음제거 방법론을 통해 기존의 시그마 필터보다 현저하게 개선된 결과를 얻을 수 있었다.

In the field of image processing, the removal noise contamination from the original image is essential. However, due to various reasons, the occurrence of the noise is practically impossible to prevent completely. Thus, the reduction of the noise contained in images remains important. In this study, we estimate the level of noise variance based on the measurement of the relative strength of the noise, and we propose a noise reduction algorithm that uses a sigma filter. As a result, the proposed statistical noise reduction methodology provides significantly improved results over the usual sigma filtering regardless of the level of the noise variance.

키워드

참고문헌

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피인용 문헌

  1. Noise reduction by sigma filter applying orientations of feature in image vol.24, pp.6, 2013, https://doi.org/10.7465/jkdi.2013.24.6.1127
  2. A Visual Quality Enhancement of Medical Image Using Optimized High-Frequency Emphasis Filter vol.18, pp.7, 2014, https://doi.org/10.6109/jkiice.2014.18.7.1681
  3. Image Noise Reduction Filter Based on Robust Regression Model vol.28, pp.5, 2015, https://doi.org/10.5351/KJAS.2015.28.5.991