5 March 1999 Nonlinear cellular neural filtering for noise reduction and extraction of image details
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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 detail 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.
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Igor N. Aizenberg, Naum N. Aizenberg, Sos S. Agaian, Jaakko T. Astola, Karen O. Egiazarian, "Nonlinear cellular neural filtering for noise reduction and extraction of image details", Proc. SPIE 3646, Nonlinear Image Processing X, (5 March 1999); doi: 10.1117/12.341075; https://doi.org/10.1117/12.341075
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