基于广义高斯最大似然估计的小波域类LMMSE滤波算法

李军侠; 水鹏朗

doi:10.3724/SP.J.1146.2006.00531

基于广义高斯最大似然估计的小波域类LMMSE滤波算法

doi: 10.3724/SP.J.1146.2006.00531

基金项目:

国家自然科学基金(60472086)和博士点基金(20050701014) 资助课题

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- 被引次数: 0
出版历程
- 收稿日期: 2006-04-21
- 修回日期: 2006-10-16
- 刊出日期: 2007-12-19

Wavelet Domain LMMSE-Like Denoising Algorithm Based on GGD ML Estimation

摘要

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。
- SAR图像去噪 /
- 广义高斯分布 /
- 次方平均值 /
- 方差估计子
Abstract: 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 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.
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参考文献(1)

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