Abstract: This paper deals with the problem of function estimation. Using the white noise model setting, we provide a method to construct a new wavelet procedure based on thresholding rules which takes advantage of the dyadic structure of the wavelet decomposition. We prove that this new procedure performs very well since, on the one hand, it is adaptive and near-minimax over a large class of B...

Segmentation of adjoining objects in a noisy image is a challenging task in image processing. Natural images often get corrupted by noise during acquisition and transmission. Segmentation of these noisy images does not provide desired results, hence de-noising is required. In this paper, we tried to address a very effective technique called Wavelet thresholding for denoising, as it can arrest t...

The basic problem suffered by a communication system is in the removal of additive background noise. Spectral Subtraction method (SSM) has been successfully implemented to suppress the background noise. This paper proposed a novel hybrid system for enhancing noise-corrupted speech which involves a parallel combination of Spectral Subtraction and soft thresholding method in wavelet domain for sp...

In this paper, we propose using Partial Differential Equation (PDE) techniques in wavelet based image processing to reduce edge artifacts generated by wavelet thresholding. We employ minimization techniques, in particular the minimization of total variation (TV), to modify the retained standard wavelet coefficients so that the reconstructed images have less oscillations near edges. Numerical ex...

Wavelet transforms enable us to represent signals with a high degree of sparsity. This is the principle behind a non-linear wavelet based signal estimation technique known as wavelet denoising. In this report we explore wavelet denoising of images using several thresholding techniques such as SUREShrink, VisuShrink and BayesShrink. Further, we use a Gaussian based model to perform combined deno...

This paper describes a method of nonlinear wavelet thresholding of time series. The Ramachandran–Ranganathan runs test is used to assess the quality of approximation. To minimize the objective function, it is proposed to use genetic algorithms one of the stochastic optimization methods. The suggested method is tested both on the model series and on the word frequency series using the Google Boo...

In this work, we are interested in developing a speech denoising tool by using a discrete wavelet packet transform (DWPT). This speech denoising tool will be employed for applications of recognition, coding and synthesis. For noise reduction, instead of applying the classical thresholding technique, some wavelet packet nodes are set to zero and the others are thresholded. To estimate the non st...

Image acquisition techniques introduce various types of artifacts and noise such as additive white gaussian noise, salt and pepper noise etc, so image denoising is an essential preprocessing step in digital image processing. It is clear from the background study of denoising, conventional methods are not much effective in reducing the noise in the image. In this work, a novel approach which int...

We consider the problem of estimating an unknown function f in a regression setting with random design. Instead of expanding the function on a regular wavelet basis, we expand it on the basis {ψjk(G), j, k} warped with the design. This allows to perform a very stable and computable thresholding algorithm. We investigate the properties of this new basis. In particular, we prove that if the desig...

In this paper a new approach for speech enhancement is presented. The proposed algorithm is based on singular value decomposition (SVD) and wavelet transform. A model of contaminant noise is estimated by using SVD in the recommended method and then, using of noise estimation determines thresholding value. Needlessness of silence frame in order to estimate the noise model is an advantage of sugg...