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基于非下采样Contourlet变换的SAR图像增强
引用本文:沙宇恒, 刘芳, 焦李成. 基于非下采样Contourlet变换的SAR图像增强[J]. 电子与信息学报, 2009, 31(7): 1716-1721. doi: 10.3724/SP.J.1146.2007.01870
作者姓名:沙宇恒  刘芳  焦李成
作者单位:西安电子科技大学信息处理研究所和智能感知与图像理解教育部重点实验室,西安,710071;西安电子科技大学信息处理研究所和智能感知与图像理解教育部重点实验室,西安,710071;西安电子科技大学信息处理研究所和智能感知与图像理解教育部重点实验室,西安,710071
基金项目:国家自然科学基金(60472084);;国家863计划项目(2007AA12Z136);;国家部委科技项目(9140A07020706DZ01)资助课题
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。

关 键 词: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
收稿时间:2007-12-03
修稿时间:2009-03-16

SAR Image Enhancement Based on Nonsubsampled Contourlet Transform
Sha Yu-heng, Liu Fang, Jiao Li-cheng. SAR Image Enhancement Based on Nonsubsampled Contourlet Transform[J]. Journal of Electronics & Information Technology, 2009, 31(7): 1716-1721. doi: 10.3724/SP.J.1146.2007.01870
Authors:Sha Yu-heng  Liu Fang  Jiao Li-cheng
Affiliation:Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China;Institute of Intelligent Information Processing;Xidian University;Xi'an 710071;China
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.
Keywords:SAR image enhancement  Geography multi-scale analysis  NonSubsampled Contourlet Transform (NSCT)  NonSubsampled Wavelet Transform(NSWT)
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