This paper proposes a spatially adaptive statistical model for wavelet image coefficients in order to perform image de-noising. The wavelet coefficients are modelled as zero-mean Gaussian random variables with high local correlation. This model is developed in a Bayesian framework, where a Maximum Likelihood (ML) estimator evaluates the variance of the blocks to which the wavelet subbands have ...

A bstract large body of work over several decades indicates that, in the presence gravitational interactions, there is loss localization resolution within a fundamental (∼ Planck) length scale ℓ . We develop general formalism based on wavelet decomposition fields that takes this UV ‘opaqueness’ into account natural and mathematically well-defined manner. This done by requiring local Lagrangian ...

Wavelet decomposition and its related nonlinear approximation problem are investigated on the basis of shift invariant spaces of functions. In particular, a Bernstein type inequality associated with wavelet decomposition is established in such a general setting. Several examples of piecewise polynomial spaces are given to illustrate the general theory. AMS Subject Classifications: 41 A 17, 41 A...

A wavelet variation of the Frequency decomposition multigrid method of Hackbusch is presented. The perfect reconstruction property of the wavelet system enable us to perform the convergence analysis of the frequency decomposition method. Some applications of this method are also presented.

In this paper we intend to optimize a wavelet-based dictionary for transient modelling with application to parametric audio coding. Transient modelling is performed by matching pursuit with an overcomplete dictionary composed of orthonormal wavelet functions that implement a wavelet-packet filter bank. We try to find the prototype filter length, the decomposition depth and the orthogonal wavele...

In this paper we propose a novel approach for image encryption supported by lossy compression using multilevel wavelet transform. We first decompose the input image using multilevel 2-D wavelet transform, and thresholding is applied on the decomposed structure to get compressed image. Then we carry out encryption by decomposing the compressed image by multi-level 2-D Haar Wavelet Transform at t...

The real world signals do not exist without noise. Wavelet Transform based denoising is a powerful method for suppressing noise in signals. In this paper, signal denoising based on Double-Density Discrete Wavelet Transform (DDDWT) and Dual-Tree Discrete Wavelet Transform (DTDWT) methods are implemented with optimum values of threshold point and level of decomposition. Based on the intensity of ...