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http://dx.doi.org/10.5351/KJAS.2018.31.5.555

Noise reduction algorithm for an image using nonparametric Bayesian 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.31, no.5, 2018 , pp. 555-572 More about this Journal
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.
Keywords
adaptive filter; Bayesian statistics; Dirichlet normal mixture model; image processing; noise reduction;
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