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http://dx.doi.org/10.5351/KJAS.2019.32.5.721

Image noise reduction algorithms using nonparametric method  

Woo, Ho-young (Department of Applied Statistics, Chung-Ang University)
Kim, Yeong-hwa (Department of Applied Statistics, Chung-Ang University)
Publication Information
The Korean Journal of Applied Statistics / v.32, no.5, 2019 , pp. 721-740 More about this Journal
Abstract
Noise reduction is an important field in image processing and requires a statistical approach. However, it is difficult to assume a specific distribution of noise, and a spatial filter that reflects regional characteristics is a small sample and cannot be accessed in a parametric manner. The first order image differential and the second order image differential show a clear difference according to the noise level included in the image and can be more clearly understood using the canyon edge detector. The Fligner-Killeen test was performed and the bootstrap method was used to statistically check the noise level. The estimated noise level was set between 0 and 1 using the cumulative distribution function of the beta distribution. In this paper, we propose a nonparametric noise reduction algorithm that accounts for the noise level included in the image.
Keywords
bootstrap; edge detector; image processing; image differencing; noise reduction; Canny edge detector; Fligner-Killeen test;
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