基于非下采样Contourlet变换的SAR图像增强

沙宇恒; 刘芳; 焦李成

doi:10.3724/SP.J.1146.2007.01870

基于非下采样Contourlet变换的SAR图像增强

doi: 10.3724/SP.J.1146.2007.01870

基金项目:

国家自然科学基金(60472084)，国家863计划项目(2007AA12Z136)和国家部委科技项目(9140A07020706DZ01)资助课题

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出版历程
- 收稿日期: 2007-12-03
- 修回日期: 2009-03-16
- 刊出日期: 2009-07-19

SAR Image Enhancement Based on Nonsubsampled Contourlet Transform

摘要

摘要: 该文基于非下采样Contourlet变换(NSCT)和SAR图像的统计特性，提出一种SAR图像增强方法，给出一种基于非下采样塔型分解的斑点噪声方差估计算法和一种基于方向邻域模型的弱边缘增强算法。该文在不同方向子代进行斑点方差估计，利用局部方向统计信息对NSCT系数并进行强边缘、弱边缘和噪声分类并进行弱边缘的增强和噪声的抑制。实验结果表明，该方法在方向信息保留和斑点抑制上优于非下采样小波变换(NSWT)相应方法。
- SAR图像增强;几何多尺度分析;非下采样Contourlet变换;非下采样小波变换
Abstract: Based on nonsubsampled Contourlet transform and SAR image statistical property, a SAR image enhancement method is proposed. A speckle noise variance estimate algorithm is given using nonsubsampled Laplace Pyramid decompose, and a wake edge enhancement algorithm using directional local neighborhood is proposed. This paper estimate the speckle variance in each decomposes direction, and the directional local neighborhood statistical is used to distinguish the strong edge, wake edge and noise. The wake edge is enhanced and the speckled noise is restrained. Experiment results show that the method represents better performance compared with NSWT in wake edges information enhancement and speckle reduction.

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