进行斑点噪声滤波是对SAR图像进行分割、分类和信息提取处理前不可或缺的处理步骤。该文首先简要回顾了各种传统的SAR图像斑点滤波算法。在充分考虑SAR图像斑点噪声乘性特征的基础上，对SAR图像进行对数变换，将乘性噪声转变为加性噪声，然后在对图像进行小波分解，采用软门限方法进行典型SAR图像斑点噪声滤波。归纳SAR图像斑点噪声滤波效果评价的5个指标，并将文中基于小波分析的滤波效果与传统的自适应局部统计斑点滤波器、Gamma-Map滤波器的滤波效果进行了全方位的比较。结果表明，该方法在图像均匀区域的辐射特性保持和斑点抑制能力，边缘、细小特征和点目标等结构信息的保持方面都优于传统的斑点滤波器。

Speckle noise is a common phenomenon in SAR images. The reduction of Speckle is necessary for any further processing of SAR image such as segmentation, classification and other procedures for information extraction. ln this paper, after a brief review of conventional filters for SAR speckle reduction, a wavelet-based soft-thresholding filter for SAR speckle reduction is presented. To evaluate the performance of this filter, the adaptive local statistics filters, which include Lee, Frost, Enhanced Lee, Enhanced Frost, Kuan, and the Gamma-Map filter, are applied to the speckle reduction for the same type SAR image. The performances are compared in several aspects including Radiometric preservation, feature preservation, speckle reduction in the extended uniform regions and the absence of artifacts. The results show that the wavelet-based speckle reduction filter performs better in every aspect in evaluation than the conventional filters do.