Statistical multirate high-resolution signal reconstruction using the empirical mode decomposition based denoising approach

Adem Ukte, Aydin Kizilkaya, M. D. Elbi
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引用次数: 6

Abstract

High-resolution signal reconstruction from a set of its noisy low-resolution measurements is considered. As an alternative solution to this problem, a method employing the empirical mode decomposition (EMD) based denoising approach is proposed. In the framework of the proposed method, iterative EMD interval-thresholding based denoising procedure is applied to each noisy low-resolution measurement so as to filter the additive white Gaussian noise effect on it. We then synthesize the noise-reduced low-resolution signals to form the high-resolution signal. Unlike the method using the Wiener filter theory for high-resolution signal reconstruction, the proposed method does not require knowledge of any correlation information about the desired high-resolution signal and its low-resolution versions. The validity of the proposed method is demonstrated by an audio signal reconstruction application.
基于经验模态分解去噪方法的统计多速率高分辨率信号重建
从一组噪声低分辨率测量数据中考虑高分辨率信号重建。为了解决这一问题,提出了一种基于经验模态分解(EMD)的去噪方法。该方法采用基于迭代EMD区间阈值的去噪方法对每一个有噪声的低分辨率测量值进行去噪处理,以滤除加性高斯白噪声对测量值的影响。然后合成降噪后的低分辨率信号形成高分辨率信号。与使用维纳滤波理论进行高分辨率信号重建的方法不同,所提出的方法不需要了解期望的高分辨率信号及其低分辨率版本的任何相关信息。通过一个音频信号重构实例验证了该方法的有效性。
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