@Article{jiot.2020.09071,
AUTHOR = {Ke Jin, Shunfeng Wang},
TITLE = {Image Denoising Based on the Asymmetric Gaussian Mixture Model},
JOURNAL = {Journal on Internet of Things},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {1--11},
URL = {http://www.techscience.com/jiot/v2n1/39676},
ISSN = {2579-0080},
ABSTRACT = {In recent years, image restoration has become a huge subject, and 
finite hybrid model has been widely used in image denoising because of its easy 
modeling and strong explanatory results. The gaussian mixture model is the most 
common one. The existing image denoising methods usually assume that each 
component of the natural image is subject to the gaussian mixture model (GMM). 
However, this approach is not entirely reasonable. It is well known that most 
natural images are complex and their distribution is not entirely gaussian. As a 
result, there are still many problems that GMM cannot solve. This paper tries to 
improve the finite mixture model and introduces the asymmetric gaussian 
mixture model into it. Since the asymmetric gaussian mixture model can 
simulate the asymmetric distribution on the basis of the gaussian mixture model, 
it is more consistent with the natural image data, so the denoising effect of the 
natural complex image is better. We carried out image denoising experiments 
under different noise scales and types, and found that the asymmetric gaussian 
mixture model has better denoising effect and performance.},
DOI = {10.32604/jiot.2020.09071}
}