新的强高斯噪声自适应滤波方法

Novel adaptive de-noising method for strong Gaussian noise

为有效去除严重的高斯噪声、更好地保护图像细节,提出了一种基于改进脉冲耦合神经网络(PCNN)的自适应去噪方法。根据PCNN神经元的点火捕获特性,定位受强噪声污染的像素,并采用类中值滤波对强噪声点进行滤除;基于无连接脉冲耦合神经网络(PCNNNI)的点火时刻矩阵自适应选择滤波方法平滑弱噪声点。实验结果表明,与传统去噪方法相比,该方法噪声去除效果好,图像细节保持完整,而且系统具有一定的泛化能力。

An adaptive de-noising method is proposed based on improved Pulse Coupled Neural Network（PCNN）.Aimed at de-noising the image with serious Gaussian noise effectively,preserving more image details,the method inlroduees a kind of detection mechanism of locating strong noised pixels based on the captures among neurons acting on image filtering, and only filters these pixels using an analogous median filter.It automatically selects the optimal filtering method to smooth the weak noised pixels based on the firing time map of PCNN with null interconnection to enhance the adaptability and de-noising ability of the system.Experimental results prove that the method based on the adaptive PCNN system can remove noise and preserve the details of images more effectively and completely than the conventional methods, and the adaptability is the feature of the system.