We are interested here in wavelet frames and their construction via multiresolution analysis (MRA); of particular interest to us are tight wavelet frames. The redundant representation offered by wavelet frames has already been put to good use for signal denoising, and is currently explored for image compression. Motivated by these and other applications, we explore in this article the theory of...

In this paper we study a generalization of the Donoho-Johnstone denoising model for the case of the translation invariant wavelet transform. Instead of soft-thresholding coeecients of the classical orthogonal discrete wavelet transform, we study soft-thresholding of the co-eecients of the translation invariant discrete wavelet transform. This latter transform is not an orthogonal transformation...

In a series of papers, Donoho and Johnstone develop a powerful theory based on wavelets for extracting nonsmooth signals from noisy data. Several nonlinear smoothing algorithms are presented which provide high performance for removing Gaussian noise from a wide range of spatially inhomogeneous signals. However, like other methods based on the linear wavelet transform, these algorithms are very ...

Starting from any two compactly supported refinable functions in L2(R) with dilation factor d, we show that it is always possible to construct 2d wavelet functions with compact support such that they generate a pair of dual d-wavelet frames in L2(R). Moreover, the number of vanishing moments of each of these wavelet frames is equal to the approximation order of the dual MRA; this is the highest...

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...

Discrete-time wavelet transform (DWT) is found to be better than other transforms in the time-varying system analysis, e.g. for time-varying parametric modelling [16], time-varying systems identification [17], time-varying parameter estimation [18] and time domain signal analysis [19]. In the literature the common method to analyze the time-varying system using discrete-time wavelet transform i...

Investigation of the dynamics of internal gravity waves often includes the interrelationship between wave amplitudes, wave speeds, and wavelengths. These relationships are commonly thought of as dispersion relations. Because internal gravity waves are the result of nonlinear dynamics dispersion relations are a challenge to obtain in a quantitative sense. Model data for internal gravity waves in...