一种改进的PCNN模型及椒盐噪声去除新算法

Yan Wu, Bing Xu, Xiao-Yue Bian
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引用次数: 1

摘要

提出了一种改进的PCNN模型PCNNPNF- Positive and Negative Firing,并提出了一种基于PCNNPNF时间矩阵的去噪算法。最大的改进是改进后的PCNN神经元输出有正放电、负放电和不放电三种状态,而PCNN只有放电和不放电两种状态。实验结果表明,基于PCNNPNF的去噪算法能够快速发现并去除两种脉冲噪声,并且比PCNN保留更多的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved PCNN model and a new removing algorithm of salt and pepper noise
An improved PCNN model-PCNN with Positive and Negative Firing, PCNNPNF-is proposed, and also put forward a de-noising algorithm based on the time matrix of PCNNPNF. The biggest improvement is that the neuron's output of improved PCNN has three states: positive firing, negative firing and no firing, while PCNN only has two states: firing and no firing. Experimental results show that the de-noising algorithm based on PCNNPNF can quickly find the two kinds of pulse noises, remove these noises, and reserve more information than PCNN.
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