一种基于PCNN赋时矩阵的图像去噪新算法

A New Algorithm for Noise Reducing of Image Based on PCNN Time Matrix

该文从图像脉冲噪声的特点出发,提出了基于脉冲耦合神经网络(Pulse Coupled Neural Networks,PCNN)赋时矩阵的图像去噪算法。赋时矩阵是由PCNN产生的一种从空间图像信息到时间信息的映射图,在图像处理中,赋时矩阵包含有与空间相联系的有用信息。计算机仿真结果表明,通过对PCNN赋时矩阵分析与处理,综合运用相关方法,可以有效地滤除被脉冲噪声污染的图像噪声,且恢复图像的视觉效果明显地好于中值滤波、均值滤波及维纳法得到的结果,其信噪比高、去噪能力强、对边缘和细节的保护性好、适应性强。

This paper puts forward a new algorithm for image reducing noise based on the time matrix of Pulse Coupled Neural Networks （PCNN） form the aspect of the characteristic of image impulsive noise. The time matrix is a mapping from spatial image information to time information generated from PCNN, The time matrix contains useful information related to spatial information in image processing. The results of computer simulations show that through analyzing and processing the PCNN time matrix, the image polluted by impulsive noise can be filtered efficiently and visual effects of restoration images are much batter than those obtained from the median filter, mean filter and wiener filter. This method presents higher Peak Signal-to-Noise, better capability to reduce noise, and can protect edges and details of images and be more adaptive.