摘要
基于小波系数服从广义高斯分布,该文采用最大似然(ML)准则估计普通图像在子带上的系数方差。该文提出的估计子是一个子带自适应因子和一个β次幂均值的乘积。与最近提出的SI-AdaptShr,LAWMAP和其它一些算法相比,所提出的算法取得了更好的去噪效果。进一步,一种简化的算法产生用于去除SAR图像的斑点噪声。这种新算法可以大大减少运算量,对大尺度的SAR图像后处理有帮助。
Based on the assumption that wavelet coefficients obey Generalized Gaussian Distribution （GGD）, this paper adopts Maximum Likelihood （ML） principle to estimate wavelet coefficients variance of common images in sub-bands. The proposed estimator is product of a sub-band adjustable factor and a β power mean factor. Compared to the recently proposed SI-AdaptShr, LAWMAP and other wavelet-based methods, better de-noising results may be obtained for the proposed method. Furthermore, a simplified algorithm is also formed to de-speckle SAR images. It is shown that the new method may remarkably reduce the calculation amount and helpful for the post-processing of large scale SAR images.
出处
《电子与信息学报》
EI
CSCD
北大核心
2007年第12期2853-2857,共5页
Journal of Electronics & Information Technology
基金
国家自然科学基金(60472086)
博士点基金(20050701014)资助课题
关键词
SAR图像去噪
广义高斯分布
口次方平均值
方差估计子
SAR image despeckling
Generalized Gaussian Distribution （GGD）
β power mean
Variance estimator
