Document Type

Article

Publication Date

6-1996

Publication Source

IEEE Transactions on Image Processing

Abstract

Extended permutation (EP) filters are defined and analyzed. In particular, we focus on extended permutation rank selection (EPRS) filters. These filters are constrained to output an order statistic from an extended observation vector. This extended vector includes N observation samples and K statistics that are functions of the observation samples. The rank permutations from selected samples in this extended observation vector are used as the basis for selecting an order statistic output. We show that by including the sample mean in the extended observation vector, the filters exhibit excellent edge enhancement properties. We also show that several previously defined classes of rank-order-based edge enhancers (CS, LUM, and WMMR sharpeners) can be formulated as subclasses of EPRS filters. These sharpening subclasses are in addition to the smoothing subclasses, which include rank conditioned rank selection, permutation stack, and weighted order statistic filters. Thus, this novel class of filters provides a broad framework within which many rank-order-based smoothers and edge enhancers can be unified. Edge enhancement properties are developed and an Ln norm EPRS filter optimization procedure is presented. Finally, extensive computer simulation results are presented, comparing the performance of EPRS and other sharpening filters in edge enhancement applications

Inclusive pages

855-867

ISBN/ISSN

1057-7149

Document Version

Postprint

Comments

The paper available for download is the authors' accepted manuscript, included in the repository in compliance with IEEE policies on archiving. Some differences may be present during the editing and layout processes. Permission documentation is on file.

Publisher

IEEE: Institute of Electrical and Electronics Engineers

Volume

5

Peer Reviewed

yes

Issue

6

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