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Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding

Kopsinis, Y., McLaughlin, S.·2009·IEEE Transactions on Signal Processing

Abstract

One of the tasks for which empirical mode decomposition (EMD) is potentially useful is nonparametric signal denoising, an area for which wavelet thresholding has been the dominant technique for many years. In this paper, the wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal. We show that although a direct application of this principle is not feasible in the EMD case, it can be appropriately adapted by exploiting the special characteristics of the EMD decomposition modes. In the same manner, inspired by the translation invariant wavelet thresholding, a similar technique adapted to EMD is developed, leading to enhanced denoising performance.

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Metadata

Authors
Kopsinis, Y., & McLaughlin, S.
Venue
IEEE Transactions on Signal Processing
Published
2009-04
Volume
57
Issue
4
Pages
1351-1362
ISSN
  • 1053-587X (ISSN-L)
  • 1941-0476
OpenAlex
W2109896502
Cited by
676
References
17
FWCI
19.06
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding. https://app.cochat.ai/papers/10.1109/tsp.2009.2013885
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Kopsinis, Y., & McLaughlin, S. (2009). Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2009.2013885

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.