基于提升小波的SAR图像斑点噪声抑制方法

SAR Image De-speckling Based on Lifting Wavelet Transform

斑点噪声去除是对合成孔径雷达(synthetic aperture radar,SAR)图像自动分割、分类、目标检测和其它定量专题信息提取处理前必要的步骤。首先简要回顾了各种传统的SAR图像斑点噪声去除方法。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,然后再对图像进行提升小波分解,采用Bayes Shrink阈值对小波系数进行处理。最后根据4个指标来对比不同方法的去噪效果。结果表明,与传统的滤波方法相比,基于提升小波的去噪方法在图像均匀区域的辐射特性保持和斑点噪声抑制能力方面具有较大的优势。与传统小波相比,提升小波不但在运算速度上有优势,而且省内存。

Speckle noise is a common phenomenon in SAR images. The reduction of Speckle is necessary for any further processing of Synthetic Aperture Radar image such as auto-segmentation, classification, object detecting and other procedures for special topic information extraction. In this paper, after a brief review of conventional filters for SAR speckle reduction, a lifting wavelet-based filter for SAR image de-speckling is presented. To evaluate the performance of this filter, four statistical measurements are used. 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 proposed filter performs better in many aspects in evaluation than the conventional filters do.