Denoising using Adaptive Thresholding and Higher Order Statistics

机译：使用自适应阈值和高阶统计量进行降噪

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We showed that a hard threshold for wavelet denoising based on higher order statistics is comparable to a second order soft threshold. The hard threshold can be made adaptive by using a third order statistic as an estimate of the noise. In addition, the relationship between an adaptive hard threshold and retaining a fraction of wavelet coefficients is shown. Qualitative and quantitative metrics based on the mean-squared error are used to compare the hard thresholding and a soft-thresholding technique, BayesShrink.

机译：我们表明，基于高阶统计量的小波去噪硬阈值可与二阶软阈值相比。通过使用三阶统计量作为噪声的估计值，可以使硬阈值具有自适应性。另外，示出了自适应硬阈值和保留小波系数的一部分之间的关系。基于均方误差的定性和定量指标可用于比较硬阈值和软阈值技术BayesShrink。

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来源
《ICA;Conference on independent component analyses, wavelets, neural networks, biosystems, and nanoengineering VII; 20090413-17;20090413-17; Orlando, FL(US);Orlando, FL(US)》|2009年|P.73430F.1-73430F.10|共10页
会议地点 Orlando FL(US);Orlando FL(US)
作者
Samuel P. Kozaitis; Tim Young;
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作者单位

Florida Institute of Technology Department of Electrical and Computer Engineering 150 W. University Blvd. Melbourne, FL 32901;

Florida Institute of Technology Department of Electrical and Computer Engineering 150 W. University Blvd. Melbourne, FL 32901;

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会议组织
原文格式 PDF
正文语种 eng
中图分类信息处理（信息加工）;
关键词
correlation; denoising; gaussian noise; higher-order statistics; thresholding; wavelet transforms;

机译：相关;降噪;高斯噪声;高阶统计;阈值;小波变换;

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Denoising using Adaptive Thresholding and Higher Order Statistics

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