一种基于脉冲耦合神经网络的脉冲噪声滤波器设计

根据脉冲噪声与其邻域中图像灰度之间的明显差异性 ,本文提出了一种新的神经网络脉冲噪声滤波器设计方案。这种脉冲耦合神经网络PCNN滤波器比现有的PCNN逐次降噪方案迭代运算次数少 ,执行速度快。并与中值滤波器、全方位结构元约束层叠滤波器、全方位结构元形态闭 开最小、开 闭最大滤波器等现有的非线性滤波器进行实验比较 ,证明 ,该方案有更好的降噪性能 ,更重要的是比这些方案更有效地保持了图像的高频细节信息。

A New Impulse Noise Filter Based on Pulse Coupled Neural Network

This paper presents a new impulse noise filter based on pulse couple d neural networks according to the apparent difference of gray value between noi sed pixels and the pixels around them. Comparing with the state-of-the-art de noi sed PCNN filter, the step by step modifying algorithm based on PCNN also, the ne w PCNN filter suggested in this paper costs less computation and less execution time. At the same time this new PCNN filter has been compared with other nonline ar filters, such as median filter, the stack filter based on omnidirectional str u ctural elements constrains, the Omnidirectional morphology Open-Closing maximum filter(OOCmax) and the Omnidirectional morphology Close-Opening minimum(OCOmi n) filter. The results of sim ulation shows that this algorithm is superior to standard median filter, the sta te-of-the-art PCNN filter, the maximal,minimal morphological filter with omn idi rectional structuring elements, and the optimal stack filter based on omnidirect ional structural elements constrains in the aspect of the impulse noise removal. What is more important is that this algorithm can keep the details of image s more effectively.