One approach to accelerating data acquisition in magnetic resonance imaging is acquisition of partial k-space data and recovery of missing data based on the sparsity of the image in the wavelet transform domain. We hypothesize that the application of stationary wavelet transform (SWT) thresholding as a sparsity-promoting operation results in improved artifact removal performance in comparison w...

The wavelet transform is a very popular tool for signal processing and analysis. It is widely used for the analysis of non-stationary, non-periodic and transient signals. The popularity of the wavelet transforms is mainly due to their ability to concentrate the energy of the processed signal into a finite number of coefficients. They are capable of providing the time-frequency localization of t...

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...

In this paper we propose a new speech enhancement technique based on the application of the Maximum a Posterior Estimator of Magnitude-Squared Spectrum (MSS-MAP) in Stationary Bionic Wavelet Domain. This technique consists at first step in applying the Stationary Bionic Wavelet Transform (SBWT) to the noisy speech signal and then applying the Maximum A Posterior Estimator of MagnitudeSquared Sp...

The fast development of digital image processing leads to the growth of feature extraction of images which leads to the development of Image fusion. Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Spatial Fusion...

Abstract-This paper presents the various methods for the spectral analysis of signals for the stationary as well as non-stationary signals. Due to non-stationary characteristics of the signals, it has been always a challenge to achieve time frequency distribution of such signals. Between the various techniques of signal analysis, this paper uses Fourier transform, Short time Fourier transform, ...

Wavelet transform provides a fine means of classifying seizure EEG signals from the normal EEG signals. Stationary wavelet transform (SWT) is used to further improve the performance of discrete wavelet transform. EEG signal prediction and classification can be bolstered up by applying SWT. In this work the residues obtained from denoising the signal using SWT is considered. Its arithmetical fac...

This is a very brief introduction on the basic concepts of wavelet transform and its applications. It is certainly not possible to present comprehensive details for wavelets in a short time, only an attempt to interject some underlying features to engineers who are not yet familiarize with wavelet transform on what makes wavelet one of the most exciting useful tools as well as research areas to...

The use of a complex wavelet transform with similar properties to the DT-CWT in seismic processing was suggested in Fernandes (2001). A wavelet basis was used for linear inversion with application to well logging in Li et. al., (1996), using a fully decimated wavelet transform and the Haar wavelet. However, the transform used does not have the shift invariance and directional selectivity proper...

Ambient vibration tests are conducted widely to estimate the modal parameters of a structure. The work proposes an efficient wavelet-based approach to determine the modal parameters of a structure from its ambient vibration responses. The proposed approach integrates the time series autoregressive (AR) model with the stationary wavelet packet transform. In addition to providing a richer decompo...