基于提升Directionlet域高斯混合尺度模型的SAR图像噪声抑制

SAR Image Denoising Based on Lifting Directionlet Domain Gaussian Scale Mixtures Model

提出了一种新的SAR图像相干斑噪声抑制方法.该方法将高斯混合尺度(GSM)模型引入Directionlet变换域,构造了基于提升Directionlet分解系数的邻域模型,并利用Bayes最小均方估计进行局部去噪.作为一种新的多尺度几何分析工具,Directionlets通过多方向选择来捕捉图像中各向异性特征,滤波器结构为可分离设计;采用提升方案进一步减小变换的运算量.文中对相邻位置和尺度的系数建立GSM模型,能较好地描述系数的边缘分布,充分体现邻域间系数的相关性.对大量真实SAR图像的去噪实验表明,文中方法取得了比空域滤波及小波方法更优的去噪性能,同时在图像边缘等细节特征保持方面具有明显优势.

In this paper, a new speckle suppression method for SAR image is proposed. By combining Directionlet transform with a version of the hidden Markov model--Gaussian scale mixtures （GSM）, the marginal distributions of neighbor coefficients in the lifting Directionlet domain are modeled. For removing the speckle noise, the Bayes least square estimation is adopted to evaluate each coefficient. Being regarded as a novel multiscale geometrical analysis tool, Directionlet transform retains the separable filtering, computation simplicity and filter design from the standard two-dimensional wavelet transform, which can capture anisotropic geometrical structures efficiently by multi-direction selection. The introduction of lifting scheme reduces computation amount greatly. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables. A Gaussian vector and a hidden positive scalar multiplier. Under this model, the marginal of neighbor coefficients are well described and the strong correlation among the amplitudes of neighbor coefficients is also presented adequately. Experiments using plentiful real SAR images indicate that the proposed method outperforms the spatial filters and other methods based on wavelets in terms of speckle reduction as well as image detail preservation.