通过分析合成孔径雷达图像的相干斑噪声模型，提出一种小波-Contourlet 与迭代Cycle spinning 相结
合的SAR 图像去噪方法. 小波-Contourlet 比小波变换、Contourlet 变换能更稀疏地表达图像，更好地获得图像
结构特征. Contourlet 变换缺乏移不变性，导致小波-Contourlet 也是缺乏移不变性的，对系数进行阈值处理会
产生伪吉布斯现象. Cycle spinning 算法可以有效地减少伪吉布斯现象，但不是最优的. 为此，用小波变换代替
LP(Laplacian pyramid) 变换作子带分解，以迭代Cycle spinning 代替多次移位取平均值. 仿真结果表明，该方法
不仅可以显著去除相干斑噪声，达到较高的峰值信噪比，而且还保留了图像的细节，改善了视觉效果.
By analyzing a speckle model of synthetic aperture radar (SAR), a de-noising method for SAR
images based on the wavelet-Contourlet transform and recursive cycle spinning is presented. Compared with
wavelet transform and Contourlet transform, wavelet-Contourlet transform can express images more sparsely
and better obtain image structure. Because the Contourlet transform lacks shift invariance, wavelet-Contourlet
transform also lacks shift invariance. Threshold processing on the coefficients may produce pseudo Gibbs
phenomena. Although a cycle spinning algorithm can reduce the pseudo Gibbs phenomena, it is not the
best. In this paper, wavelet transform is used to replace the Laplacian pyramid transform (LPT) for sub-band
decomposition. Recursive cycle spinning is used to replace the cycle spinning. Simulation results show that
the proposed algorithm is efficient, and it performs significantly better in reducing speckle noise, resulting in
higher peak signal-to-noise ratio, more image details and better visual quality.
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