Optimal morphological pattern restoration from noisy binary images

Author

Schonfeld, Dan ; Goutsias, John

Author_Institution

Dept. of Electr. & Comput. Eng., Johns Hopkins Univ., Baltimore, MD, USA

Volume

13

Issue

1

fYear

1991

fDate

1/1/1991 12:00:00 AM

Firstpage

14

Lastpage

29

Abstract

A theoretical analysis of morphological filters for the optimal restoration of noisy binary images is presented. The problem is formulated in a general form, and an optimal solution is obtained by using fundamental tools from mathematical morphology and decision theory. Consideration is given to the set-difference distance function as a measure of comparison between images. This function is used to introduce the mean-difference function as a quantitative measure of the degree of geometrical and topological distortion introduced by morphological filtering. It is proved that the class of alternating sequential filters is a set of parametric, smoothing morphological filters that best preserve the crucial structure of input images in the least-mean-difference sense

Keywords

computer vision; computerised picture processing; decision theory; filtering and prediction theory; alternating sequential filters; decision theory; degradation noise; geometrical distortion; mathematical morphology; maximum estimation procedure; mean-difference function; morphological filtering; morphological filters; morphological image analysis; morphological pattern restoration; noisy binary images; set-difference distance function; topological distortion; Degradation; Distortion measurement; Filtering theory; Image analysis; Image restoration; Minimax techniques; Noise shaping; Nonlinear distortion; Nonlinear filters; Smoothing methods;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.67627

Filename

67627