Paper Info
Title
Extended permutation filters and their application to edge enhancement.
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 L(n) 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.
Year
DOI
Venue
1995
10.1109/83.503904
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Keywords
Field
DocType
filter optimization,computer simulation results,observation samples,rank order based edge enhancers,extended observation vector,statistics,sharpening subclasses,smoothing methods,statistical analysis,order statistic,circuit optimisation,extended permutation rank selection,rank permutations,image restoration,edge enhancement,extended permutation filters,image sampling,sample mean,extended permutation rank selection filters,smoothing subclasses,digital filters,edge detection,sharpening filters,filtering theory,order statistic output,rank conditioned rank selection,image enhancement,eprs filters,weighted order statistic filters,performance,permutation stack,nonlinear filters,application software,computer simulation
Sharpening,EPRS,Digital filter,Edge detection,Artificial intelligence,Order statistic,Combinatorics,Pattern recognition,Permutation,Algorithm,Smoothing,Mathematics,Edge enhancement
Conference
Volume
Issue
ISSN
5
6
1057-7149
ISBN
Citations 
PageRank 
0-7803-2431-5
8
0.72
References 
Authors
16
2
Name
Order
Citations
PageRank
Russell C Hardie137333.68
Kenneth E Barner235439.58