Dynamics of the maximum marginal likelihood hyperparameter estimation in image restoration: Gradient descent versus expectation and maximization algorithm
نویسندگان
چکیده
منابع مشابه
Dynamics of the maximum marginal likelihood hyper - parameter estimation in image restoration : gradient descent vs
Dynamical properties of image restoration and hyper-parameter estimation are investigated by means of statistical mechanics. We introduce an exactly solvable model for image restoration and derive the differential equations with respect to macroscopic quantities. From these equations, we evaluate relaxation processes of the system to its equilibrium state in the sense of the Markov chain Monte ...
متن کاملDynamics of the maximum marginal likelihood hyper - parameter estimation in image restoration : gradient descent vs . EM algorithm
Dynamical properties of image restoration and hyper-parameter estimation are investigated by means of statistical mechanics. We introduce an exactly solvable model for image restoration and derive differential equations with respect to macroscopic quantities. From these equations, we evaluate relaxation processes of the system to the equilibrium state. Our statistical mechanical approach also e...
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We introduce a maximum Lq-likelihood estimation (MLqE) of mixture models using our proposed expectation maximization (EM) algorithm, namely the EM algorithm with Lq-likelihood (EM-Lq). Properties of the MLqE obtained from the proposed EMLq are studied through simulated mixture model data. Compared with the maximum likelihood estimation (MLE) which is obtained from the EM algorithm, the MLqE pro...
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Aggelos K, Katsaggelos, MEMBERSPIE Northwestern University McCormick School of Engineering and Applied Science Department of Electrical Engineering and Computer Science Evanston, Illinois 60208-3118 E-mail: aggk@eecs,nwu.edu Abstract. Previous work has demonstrated the effectiveness of the expectation-maximization algorithm to restore noisy and blurred singlechannel images and simultaneously id...
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The hyperparameter in image restoration by the Bayes formula is an important quantity. This communication shows a physical method for the estimation of the hyperparameter without approximation. For artificially generated images by prior probability, the hyperparameter is computed accurately. For practical images, accuracy of the estimated hyperparameter depends on the magnetization and energy o...
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ژورنال
عنوان ژورنال: Physical Review E
سال: 2001
ISSN: 1063-651X,1095-3787
DOI: 10.1103/physreve.65.016125