Student’s-t Mixture Model Based Image Denoising Method with Gradient Fidelity Term

2016 ◽  
pp. 306-311
Author(s):  
J. W. Zhang ◽  
J. Liu ◽  
Y. H. Zheng ◽  
J. Wang
Keyword(s):  
Image Denoising ◽  
Mixture Model ◽  
Denoising Method ◽  
Model Based ◽  
Fidelity Term ◽  
Student’S T

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.

Nonlinear Diffusion Equation for Image Denoising Method Based on Gradient Fidelity Term

2010 ◽  
Vol 29-32 ◽  
pp. 934-939
Author(s):  
Da Sheng Wu ◽  
Qing Qing Wen ◽  
Yu Ping Rao
Keyword(s):  
Diffusion Equation ◽  
Image Denoising ◽  
Computational Efficiency ◽  
Nonlinear Diffusion ◽  
Functional Model ◽  
Nonlinear Diffusion Equation ◽  
Visual Effects ◽  
Denoising Method ◽  
Variation Function ◽  
Fidelity Term

This article introduces the gradient fidelity term into the functional model of the image denoising to obtain a new denoising functional model and derive the relative nonlinear diffusion denoising model. The new model has been proved that the bounded variation function is integrable, which will get rid of the problem of edge leaking. According to the experimental results, the application of this model is to prevent the "ladder" effect, the result of piecewise smooth can be acquired with more natural visual effects, meanwhile, the method has been proved more stable for the value calculation and has higher computational efficiency.

scholarly journals An Expectation–Maximization-Based IVA Algorithm for Speech Source Separation Using Student’s t Mixture Model Based Source Priors

Acoustics ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 117-136 ◽  
Author(s):  
Waqas Rafique ◽  
Jonathon Chambers ◽  
Ali Sunny
Keyword(s):  
Mixture Model ◽  
Expectation Maximization ◽  
Experimental Studies ◽  
Statistical Properties ◽  
Separation Performance ◽  
Independent Vector ◽  
Unknown Parameters ◽  
Independent Vector Analysis ◽  
Model Based ◽  
Student’S T

The performance of the independent vector analysis (IVA) algorithm depends on the choice of the source prior to better model the speech signals as it employs a multivariate source prior to retain the dependency between frequency bins of each source. Identical source priors are frequently used for the IVA methods; however, different speech sources will generally have different statistical properties. In this work, instead of identical source priors, a novel Student’s t mixture model based source prior is introduced for the IVA algorithm that can adapt to the statistical properties of different speech sources and thereby enhance the separation performance of the IVA algorithm. The unknown parameters of the source prior and unmixing matrices are estimated together by deriving an efficient expectation maximization (EM) algorithm. Useful improvement in the separation performance in different realistic scenarios is confirmed by experimental studies on real datasets.

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