By Stephane Mallat

ISBN-10: 0080520839

ISBN-13: 9780080520834

ISBN-10: 012466606X

ISBN-13: 9780124666061

This publication is meant to function a useful reference for someone all in favour of the appliance of wavelets to sign processing. It has developed from fabric used to educate "wavelet sign processing" classes in electric engineering departments at Massachusetts Institute of know-how and Tel Aviv collage, in addition to utilized arithmetic departments on the Courant Institute of latest York collage and ?‰colePolytechnique in Paris. Key positive factors* presents a huge point of view at the ideas and purposes of brief sign processing with wavelets* Emphasizes intuitive realizing, whereas supplying the mathematical foundations and outline of quickly algorithms* a number of examples of actual purposes to noise elimination, deconvolution, audio and photo compression, singularity and part detection,multifractal research, and time-varying frequency measurements* Algorithms and numerical examples are carried out in Wavelab, that is a Matlab toolbox freely to be had over the web* content material is available on a number of point of complexity, reckoning on the person reader's needsNew to the second one variation* Optical move calculation and video compression algorithms* snapshot versions with bounded edition capabilities* Bayes and Minimax theories for sign estimation* two hundred pages rewritten and such a lot illustrations redrawn* extra difficulties and subject matters for a graduate direction in wavelet sign processing, in engineering and utilized arithmetic

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Extra info for A Wavelet Tour of Signal Processing, Second Edition (Wavelet Analysis & Its Applications)

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This best basis minimizes a cost function related to the non-linear approximation error of f. A fast dynamical programming algorithm can find the best basis in families of wavelet packet basis or local cosine bases [ 140]. The selected basis corresponds to a time-frequency tiling that "best" concentrates the signal energy over a few time-frequency atoms. Orthogonality is often not crucial in the post-processing of signal coefficients. One may thus further enlarge the freedom of choice by approximating the signal f with M non-orthogonal v e c t o r s {g'~m}O

8): ^ ~u,s(W) - e-iu~ x / ~ ( s ~ ) , where ~ is the Fourier transform of ~. To analyze the phase information of signals, a complex analytic wavelet is used. This means that ~(w) = 0 for w < 0. Its energy is concentrated in a positive frequency interval centered at r/. The energy of ~u,s(W) is therefore concentrated over a positive frequency interval centered at rl/s, whose size is scaled by 1/s. In the time-frequency plane, a wavelet atom ~u,s is symbolically represented by a rectangle centered at (u, rl/s).

II FOURIER K I N G D O M he story begins in 1807 when Fourier presents a memoir to the Institut de France, where he claims that any periodic function can be represented as a series of harmonically related sinusoids. This idea had a profound impact in mathematical analysis, physics and engineering, but it took one and a half centuries to understand the convergence of Fourier series and complete the theory of Fourier integrals. Fourier was motivated by the study of heat diffusion, which is governed by a linear differential equation.

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A Wavelet Tour of Signal Processing, Second Edition (Wavelet Analysis & Its Applications) by Stephane Mallat


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