Abstract: An adaptive filter was proposed to detect and remove pepper and salt noise in an image based on Pulse Coupled Neural Network (PCNN) firing matrix. The PCNN was simplified and a unidirectional decaying threshold was proposed to avoid complex parameter selection and improve processing speed. The noise-polluted pixels were detected through analyzing the PCNN firing matrix, and then only the noisy pixels were filtered by a median filter while protecting image edges and details. The window size of the filter and the filtering time were adaptively determined by calculating the noise intensity of the contaminated image. The experimental results show that the proposed method performs better in removing noise while conserving detailed information than traditional filters do.

Key words: Pulse Coupled Neural Network (PCNN), firing matrix, pepper and salt noise, image denoising, adaptive filtering

摘要： 为有效滤除灰度图像中的椒盐噪声并保留图像的边缘及细节信息，提出一种简化的阈值单向衰减脉冲耦合神经网络(PCNN)点火矩阵自适应图像滤波方法，简化的PCNN结构减少了所需参数并提高了运算速度。该方法通过对PCNN点火矩阵的分析，定位出被噪声污染的像素，只对噪声像素进行滤波，因而有效地保留了图像的细节信息；并根据椒盐噪声的特点，动态估计图像的噪声强度，自适应地选择滤波窗口的大小和滤波次数。实验结果表明提出方法较常见的图像降噪方法在滤波效果、自适应性及保留图像细节方面有明显的优势。

关键词: 脉冲耦合神经网络, 点火矩阵, 椒盐噪声, 图像去噪, 自适应滤波