Toward edge sharpening: A SAR speckle filtering algorithm
Toward edge sharpening: A SAR speckle filtering algorithm
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DOI：
10.1109/36.917910
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发表时间：
2001-04-01
影响因子：
8.2
通讯作者：
Forster, BC
中科院分区：
文献类型：
--
作者：
Dong, Y;Milne, AK;Forster, BC
本文有两个贡献。首先介绍了一种合成孔径雷达（SAR）相干斑抑制和边缘锐化算法。现有的斑点滤波算法可以有效地抑制斑点效应，但也在一定程度上模糊了图像的边缘。即使对于未经滤波的图像，仍然需要进行边缘锐化，因为SAR传感器的带宽有限，导致对突然变化的反应缓慢（模糊锐边缘）。该算法的功能作为一个自适应均值滤波器。利用高斯函数的二阶导数作为小波变换函数检测边缘交叉点。适当的伸缩尺度因子使得小波变换函数仅检测边缘交叉而忽略局部振荡。然后，在移动窗口中，如果没有边缘交叉点，则应用均值滤波器。否则，平均仅应用于由边缘交叉点分开的窗口部分，因此，该算法平滑均匀区域，同时锐化和增强边缘。滤波后的图像边缘一般比原始图像更清晰。文中分析了该滤波器与其他常用的SAR乘性噪声斑点滤波器（如Lee、Kuan和Frost滤波器）的相似性。本文的另一个贡献是从纹理保持的角度对流行的过滤器进行评估。使用一阶和二阶直方图进行评估，解释了由滤波器引起的可能的失真。
This paper makes two contributions. It first introduces an algorithm for synthetic aperture radar (SAR) speckle reduction and edge sharpening. Existing speckle filtering algorithms can effectively reduce the speckle effect but unfortunately also, to some degree, smear edges and blur images. Even for unfiltered images, there is still a need for edge sharpening, since SAR sensors have limited bandwidths,leading to slow responses to sudden changes (smearing sharp edges). The proposed algorithm functions as an adaptive-mean filter. Edge crossing points are detected by using the second-order derivative of the Gaussian function as a wavelet transform function. A proper dilation scale factor enables the wavelet transform function to detect only edge crossings and ignore the local oscillations. Then in a moving window, the mean filter is applied if there is no edge crossing point. Otherwise, averaging is only applied to the part of the window separated by edge crossing points, Consequently, the algorithm smooths uniform areas while it sharpens and enhances edges. Edges of the filtered images are generally sharper than the original, Similarities between the proposed filter and other popular speckle filters, such as the Lee, Kuan, and Frost filters, designated for SAR multiplicative noise, are analyzed. Another contribution of the paper is the evaluation of popular filters from the viewpoint of texture preservation. The evaluation is carried out using the first and second-order histograms, Possible distortions caused by filters are explained.