基于二级修复的多方向加权均值滤波算法

针对目前算法不能有效去除高概率的椒盐噪声并保护图像边缘和细节特征的缺点,提出了一种基于二级修复的多方向加权均值滤波算法。在噪声检测阶段,首先利用一个方差参数判断当前像素点与其邻域像素点之间的灰度差异程度,再通过将方差参数和灰度极值相结合的方法检测出图像中的椒盐噪声点。在噪声修复阶段,提出一种二级修复方法来修复噪声点的灰度值。首先利用改进的自适应中值滤波器对椒盐噪声点进行第一级噪声修复;然后利用方差参数将第一级修复后的噪声点划分为两类,并采用不同的修复方法对这两类像素点进行第二级噪声修复,一类像素点采用均值滤波器进行再修复,另外一类像素点采用多方向加权均值滤波器进行再修复。数值实验结果表明,所提算法的滤波性能和边缘保护能力均优于当下很多先进的滤波器。

Multi-directional Weighted Mean Denoising Algorithm Based on Two Stage Noise Restoration

In view of problem that some present algorithms cannot effectively remove salt-and-pepper noise meanwhile preserving edges and details in the case of high noise density,a multi-directional weighted mean denoising algorithm based on two stage noise restoration was proposed.In the noise detection stage,the proposed algorithm firstly introduces a variance parameter to judge the gray level difference between current pixel and its neighborhood pixels,then designs the noise detector by combining the variance parameter and gray level extreme.In the noise restoration stage,a two stage restoration method is introduced to restore the gray value of noisy pixels.Firstly,the restoration method uses the improved adaptive median filter to carry out the first stage noise restoration,then divides all the noisy pixels into two types and applies different restoration skills to carry out the second stage noise restoration.One type of noisy pixel is further restored by the mean filter and the other type of noisy pixel is further restored by the multi-directional weighted mean filter.Experimental results show that the proposed algorithm outperforms many state-of-the-art filters in terms of image denoising and edge preservation.