Open Access
ARTICLE
Image Denoising Based on the Asymmetric Gaussian Mixture Model
Ke Jin, Shunfeng Wang*
College of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
* Corresponding Author: Shunfeng Wang. Email:
Journal on Internet of Things 2020, 2(1), 1-11. https://doi.org/10.32604/jiot.2020.09071
Received 08 November 2019; Accepted 27 November 2019; Issue published 06 August 2020
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.
Keywords
Cite This Article
K. Jin and S. Wang, "Image denoising based on the asymmetric gaussian mixture model,"
Journal on Internet of Things, vol. 2, no.1, pp. 1–11, 2020.
Citations