基于城区距离的自适应加权均值滤波算法

Adaptive weighted mean filtering algorithm based on city block distance

针对传统滤波窗口不能自适应扩展以及标准均值滤波易造成图像边缘模糊的缺陷,提出一种基于城区距离的自适应加权均值滤波算法。首先,利用开关滤波思想检测出噪声点;其次,对于每一噪声点,依据城区距离扩展窗口,窗口的大小根据窗口内信号点的个数自适应地调节;最后,将窗口内足够数量信号点的灰度的加权平均值作为噪声点的灰度值,实现对噪声点的有效恢复。实验结果表明,该算法能够有效地滤除椒盐噪声,尤其对噪声密度较大的图像,去噪效果更加显著。

Concerning the defect that the traditional filtering window cannot be adaptively extended and the standard mean filter algorithm could blur edges easily, a new adaptive weighted mean filtering algorithm based on city block distance was proposed. First, the noise points can be detected with switch filtering ideas. Then, for each noise point, the window was extended according to the city block distance, and the window size was adaptively adjusted based on the number of signal points within the window. Last, the weighted mean of the signal points in the window was taken as the gray value of the noise points to achieve the effective recovery of the noise points. The experimental results show that the algorithm can effectively filter out salt-and-pepper noise, especially for the larger-noise-density image, and denoising effect is more significant.