NDLI: A New Kind of Hybrid Filter Based on the Peak-and-Valley Filter and PCNN

Content Provider	IEEE Xplore Digital Library
Author	Liu Yun Zhang Xiang-guang Wang Chuan-xu
Copyright Year	2007
Description	Author affiliation: Qingdao Univ. of Sci. & Technol., Qingdao (Liu Yun; Zhang Xiang-guang; Wang Chuan-xu)
Abstract	Pulse coupled neural network (PCNN) has gained widely research as a new artificial neural network. It derives directly from the studies of the small mammal's visual cortex. The peak-and-valley filter can keep the details of image sufficiently if the density of noise is low enough. But for the image that is badly contaminated with noise, the effect of the peak-and-valley filter is inadequate. To overcome this shortage, this paper suggests a kind of designing project of the hybrid filter that applies the ideas of the PCNN and the peak-and-valley filter. PCNN is a model with multiple parameters and finding the proper values of these parameters is an onerous task. So a simplified PCNN is put forward and its performance in removing Salt-and-pepper noise of image is discussed in this article. The theory analysis and the simulation experiments of the image processing indicate that this kind of filter can not only remove noise effectively but also keep the details of the image sufficiently.
Starting Page	36
Ending Page	39
File Size	668695
Page Count	4
File Format	PDF
ISBN	9780769529097
DOI	10.1109/SNPD.2007.442
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image processing Neurons Artificial neural networks Information filtering Pulse Coupled Neural Network Multi-layer neural network Peak-and- Valley filter Image segmentation Neural networks Non-linear filter Information filters Filtering theory Highfrequency detail. Median filter Software engineering
Content Type	Text
Resource Type	Article

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