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.

scholarly journals Image Denoising with Generalized Gaussian Mixture Model Patch Priors

2018 ◽  
Vol 11 (4) ◽  
pp. 2568-2609 ◽  
Author(s):  
Charles-Alban Deledalle ◽  
Shibin Parameswaran ◽  
Truong Q. Nguyen
Keyword(s):  
Image Denoising ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture

scholarly journals Infinite von Mises–Fisher Mixture Modeling of Whole Brain fMRI Data

2017 ◽  
Vol 29 (10) ◽  
pp. 2712-2741 ◽  
Author(s):  
Rasmus E. Røge ◽  
Kristoffer H. Madsen ◽  
Mikkel N. Schmidt ◽  
Morten Mørup
Keyword(s):  
Mixture Models ◽  
Mixture Model ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Optimal Number ◽  
Fmri Data ◽  
Directional Statistics ◽  
Inference Procedure ◽  
Whole Brain ◽  
Von Mises

Cluster analysis of functional magnetic resonance imaging (fMRI) data is often performed using gaussian mixture models, but when the time series are standardized such that the data reside on a hypersphere, this modeling assumption is questionable. The consequences of ignoring the underlying spherical manifold are rarely analyzed, in part due to the computational challenges imposed by directional statistics. In this letter, we discuss a Bayesian von Mises–Fisher (vMF) mixture model for data on the unit hypersphere and present an efficient inference procedure based on collapsed Markov chain Monte Carlo sampling. Comparing the vMF and gaussian mixture models on synthetic data, we demonstrate that the vMF model has a slight advantage inferring the true underlying clustering when compared to gaussian-based models on data generated from both a mixture of vMFs and a mixture of gaussians subsequently normalized. Thus, when performing model selection, the two models are not in agreement. Analyzing multisubject whole brain resting-state fMRI data from healthy adult subjects, we find that the vMF mixture model is considerably more reliable than the gaussian mixture model when comparing solutions across models trained on different groups of subjects, and again we find that the two models disagree on the optimal number of components. The analysis indicates that the fMRI data support more than a thousand clusters, and we confirm this is not a result of overfitting by demonstrating better prediction on data from held-out subjects. Our results highlight the utility of using directional statistics to model standardized fMRI data and demonstrate that whole brain segmentation of fMRI data requires a very large number of functional units in order to adequately account for the discernible statistical patterns in the data.

scholarly journals Detecting Vehicle Motion using Deep Gaussian Mixture Model with SCI-kit Learn

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1216-1218
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Mixture Models ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Vehicle Motion ◽  
Deep Learning Neural Network ◽  
Architecture Development

Detecting vehicle motions are a progressively significant part in road surveillance and Traffic organizing systems. This paper presents a new Deep Gaussian based mixture model that predicts accurate in detecting vehicle motions. Although the existing arrangements based on conventional Gaussian mixture model which is limited in insufficient of many distinct points which eliminate covariance and solutions relative to infinite likelihood. In the proposed scheme, the deep learning neural network is used for including the more points with nested mixture models. To overcome the effects of adding more points the modification achieved in architecture development. The validation of proposed scheme is achieved with real-time videos and process with scikit learn based model.

Sign in / Sign up

Export Citation Format

Share Document