Vol.122, No.2, 2020, pp.675-690, doi:10.32604/cmes.2020.07987
Mixed Noise Parameter Estimation Based on Variance Stable Transform
  • Ling Ding1, 2, Huyin Zhang1, *, Jinsheng Xiao3, Junfeng Lei3, Fang Xu3, Shejie Lu2
1 School of Computer Science, Wuhan University, Wuhan, 430072, China.
2 College of Computer Science and Technology, Hubei University of Science and Technology, Xianning, 437100, China.
3 School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China.
* Corresponding Authors: Huyin Zhang. Email: zhy2536@whu.edu.cn.
(This article belongs to this Special Issue: Security Enhancement of Image Recognition System in IoT based Smart Cities)
Received 17 July 2019; Accepted 25 September 2019; Issue published 01 February 2020
The ultimate goal of image denoising from video is to improve the given image, which can reduce noise interference to ensure image quality. Through denoising technology, image quality can have effectively optimized, signal-to-noise ratio can have increased, and the original mage information can have better reflected. As an important preprocessing method, people have made extensive research on image denoising algorithm. Video denoising needs to take into account the various level of noise. Therefore, the estimation of noise parameters is particularly important. This paper presents a noise estimation method based on variance stability transformation, which estimates the parameters of variance stability transformation by minimizing the noise distribution peak, and improves the parameter accuracy of mixed peak estimation by comparing and analyzing the changes of parameters. The experimental results show that the new algorithm of noise estimation has achieved good effects, which are making the field of video denoising more extensive.
Denoising technology, image quality, signal-to-noise ratio, variance stability.
Cite This Article
Ding, L., Zhang, H., Xiao, J., Lei, J., Xu, F. et al. (2020). Mixed Noise Parameter Estimation Based on Variance Stable Transform. CMES-Computer Modeling in Engineering & Sciences, 122(2), 675–690.
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