针对SAR图像斑点噪声的滤除，提出了一种新的基于Countourlet变换的快速自适应性噪声去除方法。鉴于SAR图像的Countourlet系数主要取决于斑点噪声和信号腐化，且呈现出很强的非高斯分布特性，据此，首先建立了SAR图像Countourlet系数的高斯混合分布解析模型；然后用每个系数的邻域系数通过估计其去斑收缩因子来实现系数的自适应收缩；最后对Lee滤波、Foster滤波、Gamma 滤波、小波、Curvelet和Contourlet变换的去斑性能进行了比较分析。实验结果表明，该新方法在保留细节和锐化图像的同时，能强有力地抑制斑点噪声。

A new adaptive contourlet transform-based technique for speckle removal from SAR images is presented. The distribution of the SAR image’s contourlet coefficients is mainly decided by speckle noise and useful signals corrupted. The analytic model for these distributions is proposed. The shrinkage factor for de-specking is estimated with the neighboring reference contourlet coefficient in every sub bands. Finally, the comparison of performance of Lee filter, Froster filter, Gamma filter, wavelet-based de-speckling and contourlet transform-based de-speckling is provided for both simulated and actual SAR images. It shows that the contourlet methods strongly suppress speckle, while preserving image details and sharpness.