Synthetic aperture radar image despeckling via spatially adaptive shrinkage in the nonsubsampled contourlet transform domain
Synthetic aperture radar image despeckling via spatially adaptive shrinkage in the nonsubsampled contourlet transform domain
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DOI：
10.1117/1.2841040
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Q. Sun;L. Jiao;B. Hou
中科院分区：
文献类型：
--
作者：
Q. Sun;L. Jiao;B. Hou
提出了一种新的基于非下采样轮廓波变换（NSCT）的SAR图像空域自适应去噪方法。该方法从现有的平稳小波变换（SWT）域伽玛指数似然模型结合局部空间先验模型，并扩展该模型进一步通过空间自适应收缩在NCST域的SAR图像去斑点。建议的NSCT域收缩估计由一个新的似然比函数和一个新的先验比函数，这两者都依赖于估计的NSCT系数的掩码。前者是由可变尺度和形状参数的Gamma分布和可变尺度参数的指数分布建立的，以适应NSCT的冗余特性的收缩估计。这两个分布的参数估计使用基于矩的估计。后者配备有方向邻域配置，以适应估计器的灵活的方向性的NSCT，从而提高细节保真度。我们验证了所提出的方法对真实的SAR图像，并通过与基于SWT的对应，两个经典的空间滤波器，和基于轮廓波变换的去斑技术的比较，证明了优良的去斑性能。
A new spatially adaptive shrinkage approach based on the nonsubsampled contourlet transform (NSCT) to despeckling synthetic aperture radar (SAR) images is proposed. This method starts from the existing stationary wavelet transform (SWT)–domain Gamma-exponential likelihood model combined with a local spatial prior model and extends the model further for despeckling an SAR image via spatially adaptive shrinkage in the NCST domain. The proposed NSCT-domain shrinkage estimator consists of a new likelihood ratio function and a new prior ratio function, both of which are dependent on the estimated masks for the NSCT coefficients. The former is established by the Gamma distribution with variable scale and shape parameters and the exponential distribution with variable scale parameter to adapt the shrinkage estimator to the redundancy property of the NSCT. Parameters of these two distributions are estimated by using moment-based estimators. The latter is equipped with directional neighborhood configurations to accommodate the estimator to the flexible directionality of the NSCT, and thus to enhance the detail fidelity. We validate the proposed method on real SAR images and demonstrate the excellent despeckling performance through comparisons with the SWT-based counterpart, two classical spatial filters, and the contourlet transform-based despeckling technique.