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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: email

Journal on Internet of Things 2020, 2(1), 1-11. https://doi.org/10.32604/jiot.2020.09071

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.

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APA Style
Jin, K., Wang, S. (2020). Image denoising based on the asymmetric gaussian mixture model. Journal on Internet of Things, 2(1), 1-11. https://doi.org/10.32604/jiot.2020.09071
Vancouver Style
Jin K, Wang S. Image denoising based on the asymmetric gaussian mixture model. J Internet Things . 2020;2(1):1-11 https://doi.org/10.32604/jiot.2020.09071
IEEE Style
K. Jin and S. Wang, "Image Denoising Based on the Asymmetric Gaussian Mixture Model," J. Internet Things , vol. 2, no. 1, pp. 1-11. 2020. https://doi.org/10.32604/jiot.2020.09071

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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