GPU implementation of non-local maximum likelihood estimation method for denoising magnetic resonance images

2016 ◽  
Vol 13 (1) ◽  
pp. 181-192 ◽  
Author(s):  
Adithya H. K. Upadhya ◽  
Basavaraj Talawar ◽  
Jeny Rajan
Keyword(s):  
Maximum Likelihood ◽  
Magnetic Resonance ◽  
Estimation Method ◽  
Local Maximum ◽  
Likelihood Estimation ◽  
Magnetic Resonance Images ◽  
Non Local ◽  
Local Maximum Likelihood ◽  
Gpu Implementation ◽  
Maximum Likelihood Estimation Method

scholarly journals A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps

2016 ◽  
Vol 11 (5) ◽  
pp. 913-920 ◽  
Author(s):  
P. V. Sudeep ◽  
P. Palanisamy ◽  
Chandrasekharan Kesavadas ◽  
Jan Sijbers ◽  
Arnold J. den Dekker ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Magnetic Resonance ◽  
Maximum Likelihood Estimation ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Maximum Likelihood Estimation Method

scholarly journals A Bi-Level Weibull Model with Applications to Two Ordered Events

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Shuguang Song ◽  
Hanlin Liu ◽  
Mimi Zhang ◽  
Min Xie
Keyword(s):  
Maximum Likelihood ◽  
Estimation Method ◽  
Mixture Distribution ◽  
Likelihood Estimation ◽  
Random Variable ◽  
Weibull Model ◽  
Reliability Function ◽  
The Other ◽  
Regularity Conditions ◽  
Maximum Likelihood Estimation Method

In this paper, we propose and study a new bivariate Weibull model, called Bi-levelWeibullModel, which arises when one failure occurs after the other. Under some specific regularity conditions, the reliability function of the second event can be above the reliability function of the first event, and is always above the reliability function of the transformed first event, which is a univariate Weibull random variable. This model is motivated by a common physical feature that arises fromseveral real applications. The two marginal distributions are a Weibull distribution and a generalized three-parameter Weibull mixture distribution. Some useful properties of the model are derived, and we also present the maximum likelihood estimation method. A real example is provided to illustrate the application of the model.

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