Dynamical properties of image restoration and hyper-parameter estimation

Author

Inoue, Jun-ichi ; Tanaka, Kazuyuki

Author_Institution

Graduate Sch. of Eng., Hokkaido Univ., Sapporo, Japan

fYear

2001

fDate

2001

Firstpage

383

Lastpage

392

Abstract

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 Carlo (MCMC) method. Our statistical mechanical approach also enables one to investigate the hyper-parameter estimation by means of maximization of marginal likelihood using gradient descent, or the EM algorithm from dynamical point of view

Keywords

gradient methods; image restoration; parameter estimation; statistical mechanics; Markov chain Monte Carlo method; differential equations; hyper-parameter estimation; image restoration; parameter estimation; statistical mechanics; Degradation; Differential equations; Image analysis; Image restoration; Information science; Markov random fields; Mechanical factors; Monte Carlo methods; Pixel; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing XI, 2001. Proceedings of the 2001 IEEE Signal Processing Society Workshop

Conference_Location

North Falmouth, MA

ISSN

1089-3555

Print_ISBN

0-7803-7196-8

Type

conf

DOI

10.1109/NNSP.2001.943142

Filename

943142