作　　者： ; ; ; ;

机构地区： 中国科学院电子学研究所

出　　处： 《电子学报》 2009年第12期2747-2752,共6页

摘　　要： 针对合成孔径雷达(SAR)图像相干斑噪声抑制问题,本文将双密度双树复数小波变换(DD-DT CWT)结合具有局部方差估计的双变量收缩阈值函数(BSF)构成一种新的SAR图像降噪算法实现合成孔径雷达图像降噪.首先将SAR图像用双密度双树复数小波变换进行多尺度分解,考虑小波系数间的相关性,用双变量概率密度函数作为小波系数及其父代系数的统计关性的模型,并通过Bayesian估计理论导出相应的非线性双变量收缩函数对图像不同方向的小波系数进行非线性自适应的处理,最后重建降噪后的图像.分别用仿真SAR图像和实际图像对算法进行验证,并与其它方法的性能进行比较,对不同算法处理后图像进行了主客观评价,分析结果表明,新算法的去噪效果明显优于传统的小波变换方法,不仅有效实现了图像降噪,而且较好保留了图像细节.含噪SAR图像经该算法处理后,图像性能指标均有提高. This paper presents an improved algorithm for suppressing the synthetic aperture radar image speckle noise,in which the double density dual tree complex wavelet transform（DD-DT CWT） was combined with the bivariate shrinkage function（BSF） with local variance estimation.The SAR image was firstly decomposed by the DD-DT CWT,the bivariate probability density function was used as statistic correlation model for wavelet coefficients and their parent,and the corresponding bivariate shrinkage function was obtained by Bayes＇ estimation theory. Then the wavelet coefficients were shrunk by the BSF,in which different orientations were nonlinear processed adaptively, finally the denoised image was reconstructed by all the update coefficients. The algorithm presented was tested by simulated and actual SAR image, and compared with other methods. Results are presented to verify that, the new algorithm significantly outperforms the traditional wavelet-transform-based denoising algorithm, and effectively denoises the noisy image as well as preserves the particulars. The performance index of the denoised image were simultaneously improved.

关 键 词： 合成孔径雷达图像 双密度双树复数小波变换 相干斑噪声抑制

领　　域： [自动化与计算机技术] [自动化与计算机技术]