A Wavelet Tour of Signal Processing: The Sparse Way

Hardcover | December 11, 2008

byStephane MallatEditorStephane Mallat

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Mallat's book is the undisputed reference in this field - it is the only one that covers the essential material in such breadth and depth. - Laurent Demanet, Stanford UniversityThe new edition of this classic book gives all the major concepts, techniques and applications of sparse representation, reflecting the key role the subject plays in today's signal processing. The book clearly presents the standard representations with Fourier, wavelet and time-frequency transforms, and the construction of orthogonal bases with fast algorithms. The central concept of sparsity is explained and applied to signal compression, noise reduction, and inverse problems, while coverage is given to sparse representations in redundant dictionaries, super-resolution and compressive sensing applications.Features:* Balances presentation of the mathematics with applications to signal processing* Algorithms and numerical examples are implemented in WaveLab, a MATLAB toolbox* Companion website for instructors and selected solutions and code available for studentsNew in this edition* Sparse signal representations in dictionaries* Compressive sensing, super-resolution and source separation* Geometric image processing with curvelets and bandlets* Wavelets for computer graphics with lifting on surfaces* Time-frequency audio processing and denoising* Image compression with JPEG-2000* New and updated exercisesA Wavelet Tour of Signal Processing: The Sparse Way , third edition, is an invaluable resource for researchers and R D engineers wishing to apply the theory in fields such as image processing, video processing and compression, bio-sensing, medical imaging, machine vision and communications engineering.Stephane Mallat is Professor in Applied Mathematics at Ecole Polytechnique, Paris, France. From 1986 to 1996 he was a Professor at the Courant Institute of Mathematical Sciences at New York University, and between 2001 and 2007, he co-founded and became CEO of an image processing semiconductor company.Companion website: A Numerical Tour of Signal Processing Includes all the latest developments since the book was published in 1999, including itsapplication to JPEG 2000 and MPEG-4 Algorithms and numerical examples are implemented in Wavelab, a MATLAB toolbox Balances presentation of the mathematics with applications to signal processing

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Mallat's book is the undisputed reference in this field - it is the only one that covers the essential material in such breadth and depth. - Laurent Demanet, Stanford UniversityThe new edition of this classic book gives all the major concepts, techniques and applications of sparse representation, reflecting the key role the subject pla...

Stéphane Mallat is a Professor in the Computer Science Department of the Courant Institute of Mathematical Sciences at New York University,and a Professor in the Applied Mathematics Department at ccole Polytechnique, Paris, France. He has been a visiting professor in the ElectricalEngineering Department at Massachusetts Institute of Te...

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Format:HardcoverDimensions:832 pages, 9.25 × 7.5 × 0.98 inPublished:December 11, 2008Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0123743702

ISBN - 13:9780123743701

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Table of Contents

Preface; Notations; Sparse Representations; Fourier Kingdom; Discrete Revolution; Time Meets Frequency; Frames; Wavelet Zoom; Wavelet Bases; Wavelet Packet and Local Cosine Bases; Approximations in Bases; Compression; Denoising; Sparse in Redundant Dictionaries; Mathematical Complements

Editorial Reviews

"This graduate-level textbook presents an excellently written, comprehensive survey of all major concepts, techniques, and applications of sparse representations which play a key role in signal processing" -- Manfred Tasche (Rostock), Zentralblatt MATH "There is no question that this revision should be published. Mallat's book is the undisputed reference in this field - it is the only one that covers the essential material in such breadth and depth." - Laurent Demanet, Stanford University