PCNN与灰度形态学相结合的图像去噪方法

通过总结脉冲噪声的特性，将脉冲耦合神经网络(PCNN)与数学形态学相结合，提出了一种基于PCNN与数学形态学的脉冲噪声去除方法。首先利用PCNN的同步脉冲发放特性定位脉冲噪声点的位置；然后利用数学形态学开-闭滤波对其进行去噪处理，并将结果与中值滤波、形态学开-闭滤波及PCNN与中值滤波相结合的方法进行了比较。结果表明，本文方法取得了良好的效果，但是以比中值滤波和形态学滤波更多的运算时间为代价的。在和PCNN与中值滤波相结合的方法结果相当的情况下，本文方法的运算时间较少。

A New Image De-noising Algorithm Combined PCNN with Gray-Scale Morphology

Under summarizing the characteristics of pulse noise, the pulse coupled neural network (PCNN) is combined with gray-scale morphology, and a new pulse noise removing algorithm based on PCNN and gray-scale morphology is proposed. Firstly, the noisy points are located using the synchronous pulse burst property of PCNN, then, are processed using the gray-scale open-close filtering. Finally the algorithm is compared with median filtering, gray-scale open-close filtering and with the algorithm combined PCNN with median filtering. Experimental results show that the proposed algorithm performs well。However，it is at a cost of more calculating time than median filtering and gray-scale open-close filtering. But, with similar results, it spends less time than that of the algorithm combined PCNN with median filtering.