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Estimation of the Noise Variance in Image and Noise Reduction
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 Title & Authors
Estimation of the Noise Variance in Image and Noise Reduction
Kim, Yeong-Hwa; Nam, Ji-Ho;
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 Abstract
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.
 Keywords
Bartlett test;image processing;noise;noise reduction;sigma filter;
 Language
Korean
 Cited by
1.
영상에 포함된 특징의 방향성을 적용한 시그마 필터의 잡음제거,김영화;박영호;

Journal of the Korean Data and Information Science Society, 2013. vol.24. 6, pp.1127-1139 crossref(new window)
2.
고주파 강조필터를 이용한 의료영상의 화질향상을 위한 최적화 방법,신충호;정채영;

한국정보통신학회논문지, 2014. vol.18. 7, pp.1681-1685 crossref(new window)
3.
로버스트 회귀모형에 근거한 영상 잡음 제거 필터,김영화;박영호;

응용통계연구, 2015. vol.28. 5, pp.991-1001 crossref(new window)
4.
단순선형회귀분석과 에지 검출기에 근거한 영상 잡음의 분산 추정,박영호;김영화;

Journal of the Korean Data Analysis Society, 2015. vol.17. 1B, pp.219-228
1.
A Visual Quality Enhancement of Medical Image Using Optimized High-Frequency Emphasis Filter, Journal of the Korea Institute of Information and Communication Engineering, 2014, 18, 7, 1681  crossref(new windwow)
2.
Noise reduction by sigma filter applying orientations of feature in image, Journal of the Korean Data and Information Science Society, 2013, 24, 6, 1127  crossref(new windwow)
3.
Image Noise Reduction Filter Based on Robust Regression Model, Korean Journal of Applied Statistics, 2015, 28, 5, 991  crossref(new windwow)
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Bartlett, M. A. (1937). Properties of sufficiency and statistical tests, Proceedings of the Royal Society of London, Series A, 160, 268-282. crossref(new window)

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