DocumentCode :
789017
Title :
Nonlinear multivariate image filtering techniques
Author :
Tang, Kaijun ; Astola, Jaakko ; Neuvo, Yrjo
Author_Institution :
Signal Process. Lab., Tampere Univ. of Technol., Finland
Volume :
4
Issue :
6
fYear :
1995
fDate :
6/1/1995 12:00:00 AM
Firstpage :
788
Lastpage :
798
Abstract :
In this paper, nonlinear multivariate image filtering techniques are proposed to handle color images corrupted by noise. First, we briefly review the principle of reduced ordering (R-ordering) and then define three R-orderings by selecting different central locations. Considering noise attenuation, edge preservation, and detail retention, R-ordering based multivariate filters are designed by combining the R-ordering schemes. To implement color image filtering more effectively, we develop them into a locally adaptive version. The output of the adaptive filter is the closest sample to a central location that is a weighted linear combination of the mean, the marginal median, and the center sample. As a result, we study an adaptive hybrid multivariate (AHM) filter consisting of the mean filter, the marginal median filter, and the identity filter. The performance of the two adaptive filtering techniques is compared with that of some nonadaptive ones. The examples of color image filtering show that the adaptive multivariate image filtering gives a rather good performance improvement
Keywords :
adaptive filters; adaptive signal processing; filtering theory; image colour analysis; image sampling; median filters; nonlinear filters; adaptive filter; adaptive hybrid multivariate filter; adaptive multivariate image filtering; center sample; color image filtering; detail retention; edge preservation; identity filter; marginal median; marginal median filter; mean; mean filter; noise attenuation; nonlinear multivariate image filtering; performance; reduced ordering; weighted linear combination; Adaptive filters; Attenuation; Color; Colored noise; Filtering theory; Performance analysis; Satellites; Signal processing; Signal processing algorithms; Statistics;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/83.388080
Filename :
388080
Link To Document :
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