Paper Info
Title
Nonlinear cellular neural filtering for noise reduction and extraction of image details
Abstract
Nonlinear cellular neural filters (NCNF) are based on the non-linearity of the activation functions of universal binary neuron (UBN) and multi-valued neuron (MVN). NCNF, which include the multi-valued non-linear filters (MVF) and cellular Boolean filters (CBF), their applications are presented in details in this paper. The following problems are considered in the paper: 1) NCNF in general as a class of nonlinear filters, which includes multi-valued and cellular Boolean filters based on similar complex non-linearities; 2) Multi-valued filters as a nonlinear generalization of the simple low-pass and mean filters; 3) Connection of the multi-valued filters with other nonlinear filters; 4) Cellular Boolean filters; 5)Application of the NCNF to noise reduction; 6) Application of the NCNF to the extraction of image details; 7) Application of the NCNF to precise edge detection, edge detection by narrow direction, and image segmentation.
Year
DOI
Venue
1999
10.1117/12.341075
PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE)
Keywords
Field
DocType
nonlinear filtering,noise reduction,extraction of details,edge detection
Noise reduction,Computer vision,Nonlinear system,Prototype filter,Edge detection,Linearity,Algorithm,Image processing,Filter (signal processing),Image segmentation,Artificial intelligence,Mathematics
Conference
Volume
ISSN
Citations 
3646
0277-786X
3
PageRank 
References 
Authors
0.61
0
5
Name
Order
Citations
PageRank
Igor N. Aizenberg130631.31
Naum N. Aizenberg2547.83
Sos Agaian36716.48
Jaakko Astola41515230.41
Karen Egiazarian53774207.72