提出了一种非同态滤波框架下的小波域SAR 图像相干斑抑制算法。将小波域中的后向散射信号和斑点噪声分别建模为正态逆高斯分布、高斯分布，在贝叶斯最大后验准则下推导出信号估计的表达式；为了提高模型参数的估计精度，引入多尺度局部变差系数作为异质性测度，并提出用对数正态分布对该测度进行拟合；基于异质性测度的统计分布特点，本文对小波子带中的系数进行分类，用累积量的估计方法计算每一类小波系数的模型参数。实验结果表明，与传统的同态滤波框架下的同类算法，以及与未采用分类技术的非同态滤波同类算法相比较，本文算法在主、客观性能评价上均有一定的优势，取得了较好的去斑效果。本文提出的基于异质性测度对小波系数分类的思想为SAR 图像去斑算法的研究提供了一种新的途径。

A Bayesian wavelet speckle reduction algorithm for SAR image is developed under the non-homomorphic framework. We use Normal Inverse Gaussian (NIG) function for modeling backscattered signal in wavelet domain, and Gaussian function for speckle noise (i.e. signal-dependent noise). The estimation formula of noise-free signal is derived by Bayesian maximum a posteriori (MAP) criterion. With regarding to estimation of model parameters, we introduce Multiscale Local Coefficient of Variation (MLCV) as heterogeneity measure, the histogram of which can be well fitted by logarithmic normal distribution. Based on heterogeneity measure, each coefficient in wavelet sub-band is classified into one of several different heterogeneity scenes, and NIG model parameters are computed in each class through cumulants estimation method. Experiment results show that, compared with its counterpart algorithm in homomorphic framework and its counterpart algorithm in non-homomorphic framework without heterogeneity based classification, our method has obvious advantage in terms of both subjective and objective evaluation, and has obtained satisfactory de-speckled image. A classification method of wavelet coefficients is proposed by heterogeneity measure, which could provide a new means for the research of SAR image despeckling.