Denoising using higher-order statistics

机译：使用高阶统计资料去噪

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We used a higher-order correlation-based method for signal denoising. In our approach, we determined which wavelet coefficients contained mostly noise, or signal, based on higher-order statistics. Because the higher that second-order moments of the Gaussian probability function are zero, the third-order correlation coefficient will not have a statistical contribution from Gaussian noise. We obtained results for both 1-D signals and images. In all cases, our approach showed improved results when compared to a more popular denoising method.

机译：我们使用了基于高阶相关的信号去噪方法。在我们的方法中，我们根据高阶统计量确定哪些小波系数主要包含噪声或信号。因为高斯概率函数的二阶矩越高，则零阶越高，因此三阶相关系数将不会对高斯噪声产生统计贡献。我们获得了一维信号和图像的结果。在所有情况下，与更流行的降噪方法相比，我们的方法均显示出改进的结果。

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来源
《Conference on Independent Component Analyses, Wavelets, and Neural Networks Apr 22-25, 2003 Orlando, Florida, USA》|2003年|p.37-44|共8页
会议地点 Orland FL(US)
作者
Samuel P. Kozaitis; Sunghee Kim;
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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;

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会议组织
原文格式 PDF
正文语种 eng
中图分类无线电电子学、电信技术;
关键词
denoising; wavelet transform; higher-order statistics;

机译：去噪小波变换高阶统计;

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5. Denoising of single and multidimensional signals using a higher-order statistics wavelet-based approach. [D] . Basuhail, Abdullah Ahmad Salem. 1998

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Denoising using higher-order statistics

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