Given noisy signal, its nite discrete wavelet transform is an estimator of signal's wavelet expansion coeecients. An appropriate thresholding of coeecients for further reconstruction of de-noised signal plays a key-role in the wavelet decomposition/reconstruction procedure. DJ1] proposed a global threshold = p 2 logn and showed that such a threshold asymptotically reduces the expected risk of t...

We study the problem of computing waveletbased synopses for massive data sets in static and streaming environments. A compact representation of a data set is obtained after a thresholding process is applied on the coefficients of its wavelet decomposition. Existing polynomial-time thresholding schemes that minimize maximum error metrics are disadvantaged by impracticable time and space complexi...

This paper investigates the utilization of wavelet filters via multistage convolution by Reverse Biorthogonal Wavelets (RBW) in high and low pass band frequency parts of speech signal. Speech signal is decomposed into two pass bands of frequency; high and low, and then the noise is removed in each band individually in different stages via wavelet filters. This approach provides better outcomes ...

With this article we rst like to a give a brief review on wavelet thresholding methods in non-Gaussian and non-i.i.d. situations, respectively. Many of these applications are based on Gaussian approximations of the empirical coeecients. For regression and density estimation with independent observations, we establish joint asymptotic normality of the empirical coeecients by means of strong appr...

Let {(Xi, Yi)}i∈{1,...,n} be an i.i.d. sample from the random design regression model Y = f(X) + ε with (X, Y ) ∈ [0, 1] × [−M,M ]. In dealing with such a model, adaptation is naturally to be intended in terms of L([0, 1], GX) norm where GX(·) denotes the (known) marginal distribution of the design variable X. Recently much work has been devoted to the construction of estimators that adapts in ...

The iterative thresholding algorithms started in [1] (both soft and hard) and in [2, 3, 4] (soft) for wavelet based linear inverse problems restoration with sparsity constraint. The analysis of iterate soft thresholding algorithms has been well studied under the framework of foward-backward splitting method [5, 6] and inspired many works for different applications and related minimization probl...

In diagnosis of diseases Ultrasonic devices are frequently used by healthcare professionals. The medical imaging devices namely X-ray, CT/MRI and ultrasound are producing abundant images which are used by medical practitioners in the process of diagnosis . The main problem faced by them is the noise introduced due to the consequence of the coherent nature of the wave transmitted. These noises c...

Wavelet techniques have become an attractive and efficient tool in function estimation. Given noisy data, its discrete wavelet transform is an estimator of the wavelet coefficients. It has been shown by Donoho and Johnstone (Biometrika 81 (1994) 425-455) that thresholding the estimated coefficients and then reconstructing an estimated function reduces the expected risk close to the possible min...

De-noising of SPECT and PET images is a challenging task due to the inherent low signal-to-noise ratio of acquired data. Wavelet based multiscale denoising methods typically apply thresholding operators on sub-band coefficients to eliminate noise components in spatial-frequency space prior to reconstruction. In the case of high noise levels, detailed scales of sub-band images are usually domina...

Electrocardiogram recordings (ECG) are obtained from the heart. Some sections of the recorded ECG may be corrupted by electromyography (EMG) noise from the muscle. In real situations, exercise test ECG recordings and long term recordings, are often corrupted by muscle artifacts. These EMG noise needs to be filtered before data processing. In this paper, wavelet transform is applied to remove th...