基于短距离优先加权均值的Stoilov改进算法

在使用均值滤波算法修复Stoilov相移算法中出现的奇异点时,会损失相位的细节信息,从而导致计算出的相位存在误差.针对这一问题,提出一种基于短距离优先选择原则的加权均值修正算法.首先,采用统计逼近的原则标记出奇异点;其次,采用短距离优先原则为每个奇异点构造最近邻滤波窗口,窗口的范围依非奇异点个数和当前所能取得的最短距离的情况而定;最后,用窗口中满足要求的非奇异点的加权平均值代替奇异点,实现对奇异点的修正.仿真与实验结果表明,该方法使窗口划分更加细致,能有效去除脉冲噪声,在相位分布细节处理上更具优势,且使得误差均方根低于0.06cm.

Improved Stoilov algorithm based on short distance priority and weighted mean

The details of phase information are lost in the use of mean filtering algorithm to repair singular point by using Stoilov phase shift algorithm, which leads to incorrect phase calculation. In order to solve this problem, a new weighted mean filtering algorithm based on short distance priority was proposed. First, singular points were marked by statistical approach. Then, the size of filter window, which was built basing on the short distance priority principle for each singular point, was up to the number of non-singular points and the shortest distance in current situation. Last, this algorithm used the weighted mean of the non-singular points in the window instead of the singular point to implement singular point correction. The experimental results show that window is more detailed in this method, and the proposed method can effectively remove impulse noise, especially may have advantage in protecting details of phase, and may reduce Root Mean Squared Error (RMSE) less than 0.06 cm in the actual measurement experiments.