In this paper, we propose a modified speech enhancement system using wavelet packets thresholding algorithm. First, we describe the basic algorithms of wavelet-based speech enhancement systems and we explain a previous introduced algorithm for improvement of the basic algorithm. Then we discuss on some factors affecting the performance of this system, including the thresholding function and the...

In order to removes the ring effect in traditional image denoising algorithms using wavelet thresholding, the paper analyzes the wavelet coefficients of noise images, uses second-order central moment of HH1 sub-bands as the noise variance and computes threshold values; and then performs wavelet thresholding denoising on each image block. At last, the paper weights these denoised wavelet coeffic...

Introduction: The main idea of Compressed Sensing is to exploit the fact that there is some structure and redundancy in most signals of interest. Clearly, the more we known about the signal and the more the information we encode into the signal processing algorithm, the better performance we can achieve. In this paper, we propose an adaptive compressed MRI sensing scheme that combined wavelet e...

Function approximation, which finds the underlying relationship from a given finite input-output data is the fundamental problem in a vast majority of real world applications, such as prediction, pattern recognition, data mining and classification. Various methods have been developed to address this problem, where one of them is by using artificial neural networks. In this paper, the radial bas...

We consider the phase retrieval problem in which one tries to reconstruct a function from the modulus of its wavelet transform. We study the unicity and stability of the reconstruction. In the case where the wavelets are Cauchy wavelets, we prove that the modulus of the wavelet transform uniquely determines the function up to a global phase. We show that the reconstruction operator is continuou...

This paper proposes a comparison between wavelet neural networks (WNN), RBF neural network and polynomial approximation in term of 1-D and 2-D functions approximation. We present a novel wavelet neural network, based on Beta wavelets, for 1-D and 2-D functions approximation. Our purpose is to approximate an unknown function f: Rn R from scattered samples (xi; y = f(xi)) i=1....n, where first, w...

The translated function with any integer multiple of the sampling period is completely represented in the wavelet space by one of the Overcomplete Discrete Wavelet Transform (ODWT ) members. This theoretical result leads to a new motion estimation and motion compensation scheme working in the wavelet transform domain. Our experiments, performed on real image sequences, show high quality and low...

The wavelet transform of a function f(t) is deened by the formula: Wf(t; a) = W a f(t) = 1 p a Z f(s)g(t ? s a) ds where g(t) is a xed function, t 2 R and a 2 R +. This transform yields a joint timescale representation the original input function that has been of great recent interest. (See for example D1] D2] and HW]). In a recent correspondence, Flandrin F] proposed the use of the wavelet tra...

Time series analysis is the statistical approach used to analyze a of data. most popular method for forecasting, which widely in several and economic applications. The wavelet transform powerful mathematical technique that converts an analyzed signal into time-frequency representation. provides information both time domain frequency domain. aims this study are propose function by derivation quo...

We introduce a wavelet-based model of local stationarity. This model enlarges the class of locally stationary wavelet processes and contains processes whose spectral density function may change very suddenly in time. A notion of time-varying wavelet spectrum is uniquely defined as a wavelet-type transform of the autocovariance function with respect to so-called autocorrelation wavelets. This le...