Abstract

Spatial filtering of two-dimensional pictorial data as an extension of one-dimensional filter theory is applied to the problem of enhancing the detection of localized objects which are superimposed upon a noisy background.

Four types of filters are derived. These are the linear, quadratic, general statistical, and decision filters. Each filter is of the “matched” type, the different designs being associated with various degrees of knowledge about the noise statistics.

A computer simulation of the linear and general statistical filters was done and examples are shown.

© 1962 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Image classification at low light levels

Miles N. Wernick and G. Michael Morris
J. Opt. Soc. Am. A 3(12) 2179-2187 (1986)

Detection of gratings and small features in speckle imagery

Vijaya N. Korwar and John R. Pierce
Appl. Opt. 20(2) 312-319 (1981)

Image detection and enhancement

Adolph Baker
Appl. Opt. 20(22) 3917-3922 (1981)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (14)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Equations (55)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription