Regularization Parameter Selection for Gaussian Mixture Model Based Image Denoising Method

2016 ◽  
pp. 291-297
Author(s):  
J. W. Zhang ◽  
J. Liu ◽  
Y. H. Zheng ◽  
J. Wang
Keyword(s):  
Image Denoising ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Regularization Parameter ◽  
Gaussian Mixture ◽  
Parameter Selection ◽  
Denoising Method ◽  
Model Based ◽  
Selection For ◽  
Regularization Parameter Selection

scholarly journals Image Denoising Based on Improved Gaussian Mixture Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hui Wei ◽  
Wei Zheng
Keyword(s):  
Mixture Models ◽  
Image Denoising ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Denoising Method ◽  
L2 Norm ◽  
Image Area ◽  
The Difference

An image denoising method is proposed based on the improved Gaussian mixture model to reduce the noises and enhance the image quality. Unlike the traditional image denoising methods, the proposed method models the pixel information in the neighborhood around each pixel in the image. The Gaussian mixture model is employed to measure the similarity between pixels by calculating the L2 norm between the Gaussian mixture models corresponding to the two pixels. The Gaussian mixture model can model the statistical information such as the mean and variance of the pixel information in the image area. The L2 norm between the two Gaussian mixture models represents the difference in the local grayscale intensity and the richness of the details of the pixel information around the two pixels. In this sense, the L2 norm between Gaussian mixture models can more accurately measure the similarity between pixels. The experimental results show that the proposed method can improve the denoising performance of the images while retaining the detailed information of the image.

scholarly journals Mechanical Vibration Signal Denoising Using Quantum-Inspired Standard Deviation Based on Subband Based Gaussian Mixture Model

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Aidong Xu ◽  
Wenqi Huang ◽  
Peng Li ◽  
Huajun Chen ◽  
Jiaxiao Meng ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Gaussian Mixture ◽  
Wavelet Coefficients ◽  
Denoising Method ◽  
Reduction Effect ◽  
Thresholding Function

Aiming at improving noise reduction effect for mechanical vibration signal, a Gaussian mixture model (SGMM) and a quantum-inspired standard deviation (QSD) are proposed and applied to the denoising method using the thresholding function in wavelet domain. Firstly, the SGMM is presented and utilized as a local distribution to approximate the wavelet coefficients distribution in each subband. Then, within Bayesian framework, the maximum a posteriori (MAP) estimator is employed to derive a thresholding function with conventional standard deviation (CSD) which is calculated by the expectation-maximization (EM) algorithm. However, the CSD has a disadvantage of ignoring the interscale dependency between wavelet coefficients. Considering this limit for the CSD, the quantum theory is adopted to analyze the interscale dependency between coefficients in adjacent subbands, and the QSD for noise-free wavelet coefficients is presented based on quantum mechanics. Next, the QSD is constituted for the CSD in the thresholding function to shrink noisy coefficients. Finally, an application in the mechanical vibration signal processing is used to illustrate the denoising technique. The experimental study shows the SGMM can model the distribution of wavelet coefficients accurately and QSD can depict interscale dependency of wavelet coefficients of true signal quite successfully. Therefore, the denoising method utilizing the SGMM and QSD performs better than others.

A Gaussian mixture model based virtual sample generation approach for small datasets in industrial processes

2021 ◽  
Vol 581 ◽  
pp. 262-277
Author(s):  
Ling Li ◽  
Seshu Kumar Damarla ◽  
Yalin Wang ◽  
Biao Huang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Industrial Processes ◽  
Virtual Sample ◽  
Model Based
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