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张 伟1, 刘文波1, 张 弓2, 时文华2(1.南京航空航天大学自动化学院,南京 210016;2.南京航空航天大学信息科学与技术学院,南京 210016)

摘 要
提出了一种基于Nonsubsampled Contourlet(NSCT)变换域自适应收缩的SAR图像相干斑抑制算法。首先将SAR图像分解至NSCT域,其次对NSCT系数进行Pizurica自适应收缩。利用NSCT变换的良好的方向选择性及平移不变性,同时结合Pizurica自适应收缩的方向空间相关性及其局部噪声度量,自适应地得到各方向的高频子带系数对应的收缩因子,修正NSCT系数,最终将修正后的子带系数通过NSCT逆变换获得经过斑点噪声抑制的图像。实验结果表明,与小波域软阈值和Contourlet域软阈值算法相比,该算法在有效抑制SAR图像斑点噪声的同时能更好、更清晰地保持图像的边缘细节特征。
SAR Image Denoising Algorithm Based on Adaptive Shrinkage in Nonsubsampled Contourlet Domain

ZHANG Wei1, LIU Wenbo1, ZHANG Gong2, SHI Wenhua2(1.College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016;2.College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics ,Nanjing 210016)

A new algorithm for SAR image denoising based on adaptive shrinkage in Nonsubsampled Contourlet Domain, is presented . The nonsubsampled contourlet coefficients of SAR images are reduced by the corresponding Pizurica adaptive shrinkage factor. The Pizurica shrinkage factor takes into account not only the local noise measure ,but also prior directional spatial consistency, and combines the shift-invariance and direction selectivity of the nonsubsampled contourlet transform. The shrinkage factor is assembled at each high frequency subband to modify the nonsubsampled contourlet coefficients. Inverse nonsubsampled contourlet transform is performed on the updated coefficients to get the denoised image. Compared with the denoising methods based on wavelet soft-threshold and contourlet soft-threshold ,the proposed algorithm can reduce speckle noise more effectively while preserving the edges of the SAR images.