基于改进PLOW算法的干涉相位滤波

针对合成孔径雷达干涉相位滤波问题，提出了一种改进的分块局部最佳维纳滤波算法（Patch-based Locally Optimal Wiener, PLOW）。该算法是加性高斯白噪声下的线性最小均方误差估计（Linear Minimum Mean Square Estimation, LMMSE），利用目前图像滤波最前沿的技术——非局部技术，来联合估计图像的一、二阶矩。针对干涉相位中噪声的空变性，在应用中提出了两点改进：首先，估计噪声的标准差时，用均值代替中值；其次，根据噪声标准差的最大值和均值的比值，自适应的确定类的数量。经过仿真和实测数据验证，改进后的PLOW算法是有效的，并优于其他三种算法。

Modified PLOW for interferometric phase filtering

Based on the interferometric phase filtering problem, a modified patch-based locally optimal wiener (PLOW) method was proposed. This method was the linear minimum mean square error estimator under the Gaussian additive noise condition and jointly estimated the moments, namely, mean and covariance using non-local means which was the state-of-art technique. When applied to interferometric phase filtering, two modifications were proposed according to the spatial variation of the noise. First, mean value, instead of median value, was used in the estimation of the noise standard deviation. Second, the number of clusters was determined adaptively according to the ratio of the maximum value to the mean value of the noise standard deviation. Experimental results on both simulated and real data confirmed that the modified PLOW was effective and can obtain better results than the other three methods does.