Abstract: This paper puts forward a new method based on Hilbert-Huang Transform (HHT) and Wavelet denoising for extracting feature information of voltage flicker signal. Firstly, envelope signal could be extracted by using HHT from original signal containing noises. Then the noises and higher harmonic components of envelope signal could be removed by using Wavelet denoising. Finally, the noise-...

Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly Gaussian. These models are unrealistic for many real-world signals. In this paper, we develop a new framework for statistical signal processing based on wavelet-domain hidden Markov models (HMM’s) that concisely models the statistical dependen...

Wavelet Packet Transforms (WPT) is a particular linear combinations of wavelet, now becoming an efficient tool for signal analysis. Compared with the normal wavelet analysis, it has special abilities to achieve higher discrimination by analyzing the higher frequency domains of a signal. The frequency domains divided by the wavelet packet can be easily selected and classified according to the ch...

In this paper, a denoising approach, which exploits patchredundancy for removing Gaussian noise from RGB color images is described. Both geometrical and photometrical similarity of image patches have to be considered for learning the parameters of this Patch-based Locally Optimal Weiner(PLOW) filer. K-means clustering,with LARK(Locally Adaptive Regression Kernel) features, is used to identify t...

The denoising method based on empirical mode decomposition (EMD) can be broadly divided into: IMF extraction method and IMF threshold approach. Aiming to the problems of how to select IMFs in extraction method and the processing of the selected IMFs, a threshold denoising method based on EMD is put forward. In this method, the standard of IMF selection in energy viewpoint is offered, and the IM...

In electron tomographic reconstructions of biological specimens the information about their structure is not directly accessible since most of the signal is buried in noise. An interpretation of the images using surface and volume rendering techniques is difficult due to the noise sensitivity of rendering algorithms. We explore the use of various multiscale representations for denoising 2D and ...

Principles of wavelet shrinkage denoising are reviewed. Both 1-D and 2-D examples are demonstrated. The performance of various ideal and practical Fourier and wavelet based denoising procedures are evaluated and compared in a new Monte Carlo simulation experiment. Finally, recommendations for the practitioner are discussed. 1 Some Opposing Viewpoints Applied scientists and engineers who work wi...