Paper Info
Title
A clustering filter for scale-space filtering and image restoration
Abstract
A nonlinear clustering filter is derived using the maximum entropy principle. This filter is governed by a single-scale parameter and uses local characteristics in the data to determine the scale parameter in the output space. It provides a mechanism for removing impulsive noise, preserving edges, and improving smoothing of nonimpulsive noise. It also presents a scheme for nonlinear scale-space filtering. Comparisons with Gaussian scale-space filtering are made using real images. It is demonstrated that the clustering filter gives much better results
Year
DOI
Venue
1993
10.1109/CVPR.1993.341036
CVPR
Keywords
Field
DocType
scale parameter,output space,parameter estimation,edge preservation,filtering and prediction theory,image restoration,image recognition,maximum entropy principle,image reconstruction,nonlinear scale-space filtering,nonimpulsive noise smoothing,entropy,nonlinear clustering filter,impulsive noise removal,single-scale parameter,anisotropic magnetoresistance,filtering,impulse noise,speech processing,computer vision,space technology,scale space
Computer vision,Root-raised-cosine filter,Median filter,Pattern recognition,Computer science,Salt-and-pepper noise,Filter (signal processing),Filtering problem,Kernel adaptive filter,Artificial intelligence,Cluster analysis,Filter design
Conference
Volume
Issue
ISSN
1993
1
1063-6919
Citations 
PageRank 
References 
2
0.46
4
Authors
1
Name
Order
Citations
PageRank
Yiu-fai Isaac Wong16216.77