IEEE Transactions on Signal Processing · 2009 · 674 citations · 19 references
EngineeringBiomedical EngineeringElectrical EngineeringBiomedical ImagingBiomedical Signal ProcessingDigital ImagingDigital Image ProcessingDigital Signal ProcessingImage EnhancementImage ProcessingImage RestorationSignal ProcessingDenoisingEmpirical Mode DecompositionImage DenoisingSignal DenoisingSignal ThresholdingThresholdingWavelet AnalysisWavelet Denoising
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.
19
Norden E. Huang, Steven Long, Man‐Li C. Wu et al. · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 1998 · 23.3K citations · Full text
Computing, Computational Engineering, Biomedical Signal Processing +16
Ideal spatial adaptation by wavelet shrinkage
David L. Donoho, Iain M. Johnstone · Biometrika · 1994 · 7.8K citations
Ideal Spatial Adaptation by Wavelet Shrinkage
David L. Donoho, Iain M. Johnstone · Biometrika · 1994 · 2.5K citations