基于改进伪中值滤波器的道路图像滤波算法

摘要

To address the shortcomings of the existing cellular neural network (CNN)-based filters, such as slow convergence,poor stability, and relatively vague filtered images, this papaer designed an improved pseudo-median filter, based on differencecontrolled CNN. It proposed and tested four improved measures, including changing the value space, introducing random perturbation, expanding the median filter window, and using the Mask method. The experimental results demonstrate that the new algorithm can better filter out random pulse noise of various intensities, reserve the image details, and has the good real-time.%针对已有的细胞神经网中值滤波器滤波时,收敛速度慢、稳定性不好以及滤波图像比较模糊的缺点,设计一种差值控制细胞神经网的改进伪中值滤波器.提出了改变取值空间、引入随机扰动、扩大中值滤波窗口尺度和引入Mask掩图的改进方法.实验结果表明,该算法具有去除各种强度脉冲随机噪声能力,又能保护图像细节信息,而且具有良好的实时性.