The core of the wavelet approach to nonparametric regression is thresholding of wavelet coefficients. This paper reviews a cross-validation method for the selection of the thresholding value in wavelet shrinkage of Oh, Kim, and Lee (2006), and introduces the R package CVThresh implementing details of the calculations for the procedures. This procedure is implemented by coupling a conventional c...

One of the fields where wavelets have been successfully applied is data analysis. Beginning in the 1990s, wavelets have been found to be a powerful tool for removing noise from a variety of signals (denoising). They allow to analyse the noise level separately at each wavelet scale and to adapt the denoising algorithm accordingly. Wavelet thresholding methods for noise removal, in which the wave...

Many important problems in engineering and science are well-modeled by Poisson processes. A common technique for noise reduction is to use a filter. Wiener filter, which utilizes the second-order statistics of the Fourier decomposition, and the median filter which is based on the theory of order statistion, are normally used to denoise degraded images. Wavelet-based methods are powerful tools f...

Recommended by Satya Dharanipragada Wavelet denoising is commonly used for speech enhancement because of the simplicity of its implementation. However, the conventional methods generate the presence of musical residual noise while thresholding the background noise. The unvoiced components of speech are often eliminated from this method. In this paper, a novel algorithm of wavelet coefficient th...

A new adaptive speech enhancement system, which utilizes a second-generation wavelet transform (SGWT) decomposition and a novel adaptive subband thresholding technique, is presented. The adaptive thresholding technique is based on accurate estimation of subband segmental signal-to-noise ratio (SegSNR) and voiced/unvoiced classification of the speech. First, the speech signal is segmented and ea...

Recent advancements in data collection allow scientists and researchers to obtain massive amounts of information in short periods of time. Often this data is functional and quite complex. Wavelet transforms are popular, particularly in the engineering and manufacturing fields, for handling these types of complicated signals. A common application of wavelets is in statistical process control (SP...

in recent years, sub-band speech recognition has been found useful in addressing the need for robustness in speech recognition, especially for the speech contaminated by band-limited noise. in sub-band speech recognition, the full band speech is divided into several frequency sub-bands, with the result of the recognition task given by the combination of the sub-band feature vectors or their lik...

A data-driven block thresholding procedure for wavelet regression is proposed and its theoretical and numerical properties are investigated. The procedure empirically chooses the block size and threshold level at each resolution level by minimizing Stein’s unbiased risk estimate. The estimator is sharp adaptive over a class of Besov bodies and achieves simultaneously within a small constant fac...

This paper introduces a novel speckle reduction method based on thresholding the wavelet coeecients of the logarithmically transformed image. The method is computational eecient and can signiicantly reduce the speckle while preserving the resolution of the original image. Both soft and hard thresholding schemes are studied and the results are compared. When fully polarimetric SAR images are ava...