A renormalization group approach to image processing problems

Author

Gidas, Basilis

Author_Institution

Div. of Appl. Math., Brown Univ., Providence, RI, USA

Volume

11

Issue

2

fYear

1989

fDate

2/1/1989 12:00:00 AM

Firstpage

164

Lastpage

180

Abstract

A method for studying problems in digital image processing, based on a combination of renormalization group ideas, the Markov random-field modeling of images, and metropolis-type Monte Carlo algorithms, is presented. The method is efficiently implementable on parallel architectures, and provides a unifying procedure for performing a hierarchical, multiscale, coarse-to-fine analysis of image-processing tasks such as restoration, texture analysis, coding, motion analysis, etc. The method is formulated and applied to the restoration of degraded images. The restoration algorithm is a global-optimization algorithm applicable to other optimization problems. It generates iteratively a multilevel cascode of restored images corresponding to different levels of resolution, or scale. In the lower levels of the cascade appear the large-scale features of the image, and in the higher levels, the microscopic features of the image

Keywords

Markov processes; Monte Carlo methods; computerised picture processing; optimisation; Markov random-field modeling; Monte Carlo algorithms; coarse-to-fine analysis; computerised picture processing; digital image processing; global-optimization; image restoration; multilevel cascode; renormalization group; texture analysis; Digital images; Image analysis; Image motion analysis; Image processing; Image restoration; Image texture analysis; Iterative algorithms; Monte Carlo methods; Parallel architectures; Performance analysis;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.16712

Filename

16712