基于Contourlet变换的自适应SAR图像相干斑噪声抑制算法

为了保持高岛分辨率合成孔径雷达(SAR)图像中的纹理结构,提出了一种基于BivaShrink模型的Contourlet 域SAR图像相干斑噪声抑制算法.联合当前层和父层的Contourlet系数,通过计算局部方差一致性范数和区域能量比,自适应地确定方差估计区域的形状和大小.从而对原始图像方差进行最优估计.实验结果表明,算法在噪声的去除和结构信息等细节的保持上均不同程度的优于小波BivaShrink去噪算法和Contourlet 阈值去噪算法,主观效果和数值指标都有较好改进.

The Coherent Speckle Suppression Method in SAR image Based on Contourlet Domain Adaptive Biva Shrink Model

In order to preserve the textural feature affected by multiplicative speckle in high resolution synthetic aperture radar (SAR) images, a despeckling metod, based on contourlet domain adaptive BivaShrink denoising model, is proposed to suppress the speckle in SAR images. By calculating the variance homogeneous measurement and local energy ratio, the shape and size of variance estimating windows are determined adaptively, and then the variance of original images can be estimated optimally. Expriments show that the proposed algorithm performs better on not only speckle reduction but also preservation of structural detail information than wavelet BivaShrink algorithm and contourlet threshold algorithm. Visual effect and experimental numerical index both are improved apparently.