A Class of Multivariate Denoising Algorithms Based on Synchrosqueezing
A Class of Multivariate Denoising Algorithms Based on Synchrosqueezing
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DOI：
10.1109/tsp.2015.2404307
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发表时间：
2015-05-01
影响因子：
5.4
通讯作者：
Mandic, Danilo P.
中科院分区：
文献类型：
--
作者：
Ahrabian, Alireza;Mandic, Danilo P.
基于高分辨率时频算法的单变量阈值技术，如同步压缩变换，已经成为从真实的世界数据中去除噪声的重要工具。低成本的多通道传感器技术突出了直接多元去噪的需要，为此，我们介绍了一类基于同步压缩变换的多元去噪技术。这是通过划分时频域以识别多变量数据内的组成数据通道所共有的一组调制振荡，以及通过采用修改的通用阈值以去除噪声分量，同时保留感兴趣的信号分量来实现的。利用这一原理，介绍了基于小波和傅立叶变换的多元同步压缩去噪算法。所提出的多变量去噪算法的性能说明了合成和真实的世界的数据。
Univariate thresholding techniques based on high resolution time-frequency algorithms, such as the synchrosqueezing transform, have emerged as important tools in removing noise from real world data. Low cost multichannel sensor technology has highlighted the need for direct multivariate denoising, and to this end, we introduce a class of multivariate denoising techniques based on the synchrosqueezing transform. This is achieved by partitioning the time-frequency domain so as to identify a set of modulated oscillations common to the constituent data channels within multivariate data, and by employing a modified universal threshold in order to remove noise components, while retaining signal components of interest. This principle is used to introduce both the wavelet and Fourier based multivariate synchrosqueezing denoising algorithms. The performance of the proposed multivariate denoising algorithm is illustrated on both synthetic and real world data.