By coupling the wavelet transform with a particular nonlinear shrinking function, the Red-telescopic optimal wavelet estimation of the risk (TOWER) method is introduced for removing noise from signals. It is shown that the method yields convergence of the 2 risk to the actual solution with optimal rate. Moreover, the method is proved to be asymptotically efficient when the regularization parame...

Speech signal analysis is one of the important areas of research in multimedia applications. Discrete Wavelet technique is effectively reduces the unwanted higher or lower order frequency components in a speech signal. Wavelet-based algorithm for audio de-noising is worked out. We focused on audio signals corrupted with white Gaussian noise which is especially hard to remove because it is locat...

The partitioning of turbulent perturbations into a \low-dimensional" part responsible for much of the turbulent energy and uxes and a \high-dimensional" passive part that contributes little to turbulent energy and transport dynamics is investigated using atmospheric surface layer (ASL) measurements. It is shown that such a partitioning scheme can be achieved by transforming the ASL measurements...

We consider the recent suggestion that spectrum estimation can be accomplished by applying wavelet denoising methodology to wavelet packet coefficients derived from the logarithm of a spectrum estimate. The particular algorithm we consider consists of computing the logarithm of the multitaper spectrum estimator, applying an orthonormal transform derived from a wavelet packet table to the log mu...

Wavelet theory has been extensively developed in the function space L2 and discrete wavelet transform has successful applications in many areas. However, to understand better the performance of different discrete wavelet transforms, it is important to investigate their underlying discrete wavelet systems in l2. Though some preliminary results have been found recently, despite the fact that stab...

In many applications it is desirable to study nonorthogonal wavelet transforms. A translation-invariant wavelet transform is a nonorthogonal variant of the classical wavelet transform which plays an important role in denoising algorithms. However, it has been observed in many experiments that the thresholding scheme for the orthogonal DWT should be slightly modiied for use in the translation-in...

This paper presents an approach which addresses both de-noising and contrast enhancement. In a multiscale wavelet analysis framework, we take advantage of both soft thresholding and hard thresholding wavelet shrinkage techniques to reduce noise. In addition, we carry out nonlinear processing to enhance contrast within structures and along boundaries. Feature restoration and enhancement are acco...

Estimating the images using decimated wavelet transform is very popular technique in different applications. In this paper a new thresholding function with combination of Smoothly Clipped Absolute Deviation (SCAD), Hard thresholding and soft thresholding functions are introduced for wavelet based denoising of images. The proposed technique is applied for denoising of noisy images contaminated w...

Wavelet thresholding as introduced by Donoho and Johnstone (1994) provides a simple technique which involves thresholding the output of the Discrete Wavelet Transform (DWT) of the data of interest, as a way of removing noise from the signal. They show that their estimators are asymptotically minimax for a wide range of norms and spaces. This convergence requires strong regularization or thresho...

An approach for speckle reduction and feature enhancement under a framework of multiscale wavelet analysis is presented. The advantages of both soft thresholding and hard thresholding wavelet shrinkage techniques are utilized to eliminate noise and preserve the sharpness of salient features. We integrate a method of wavelet shrinkage with nonlinear processing to enhance contrast within structur...