In traditional denoising techniques, filters and Short time Fourier transform are not so good for speech signal denoising. Wavelet thresholding de-noising techniques provide a new way to reduce background noise in speech signal. However, the soft thresholding is best in reducing noise but worst in preserving edges, and hard thresholding is best in preserving edges but worst in de-noising. In th...

In this thesis, we investigate some adaptive wavelet approaches for a so-called nonparametric regression model with strongly dependent Gaussian residuals. At first, we discuss data adaptive wavelet estimation of a trend function. It turns out that under certain smoothing conditions on the trend function, the asymptotic rate of the mean integrated square error (MISE) of a trend estimator obtaine...

We propose using Partial Differential Equation (PDE) techniques in wavelet based image processing to remove noise and reduce edge artifacts generated by wavelet thresholding. We employ a variational framework, in particular the minimization of total variation (TV), to select and modify the retained wavelet coefficients so that the reconstructed images have fewer oscillations near edges while no...

The sparseness and decorrelation properties of the discrete wavelet transform have been exploited to develop powerful denoising methods. Most schemes use arbitrary thresholding nonlinearities with ad hoc parameters, or employ computationally expensive adaptive procedures. We overcome these de ciencies with a new wavelet-based denoising technique derived from a simple empirical Bayes approach ba...

In this paper, wavelet signal processing technique is applied to improving navigation signals. Low cost MEMS signals can be distorted with conventional pre-filtering method such as low-pass filtering which causes unwanted smoothing on real signals. However, wavelet thresholding method does not distort the rapidly-changing signal but reduces signal noise. This paper has applied thresholding meth...

In Oh, Naveau and Lee (2001) a simple method is proposed for reducing the bias at the boundaries for wavelet thresholding regression. The idea is to model the regression function as a sum of wavelet basis functions and a low-order polynomial. The latter is expected to account for the boundary problem. Practical implementation of this method requires the choice of the order of the low-order poly...

Wavelet shrinkage and thresholding methods constitute a powerful way to carry out signal denoising, especially when the underlying signal has a sparse wavelet representation. They are computationally fast, and automatically adapt to the smoothness of the signal to be estimated. Nearly minimax properties for simple threshold estimators over a large class of function spaces and for a wide range o...

In this paper a hybrid denoising algorithm which combines spatial domain bilateral filter and hybrid thresholding function in the wavelet domain is proposed. The wavelet transform is used to decompose the noisy image into its different subbands namely LL, LH, HL, and HH. A two stage spatial bilateral filter is applied. The first stage is applied on the noisy image before wavelet decomposition. ...

Wavelet techniques have become an attractive and eecient tool in function estimation. Given noisy data, its discrete wavelet transform is an estimator of the wavelet coeecients. It has been shown by Donoho and Johnstone (1994) that thresholding the estimated coeecients and then reconstructing an estimated function reduces the expected risk close to the possible minimum. They ooered a global thr...