By
Stephane Mallat, École Polytechique, Centre de Mathématiques Appliquées, Paris, France
Description
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
The 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 students
New 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 exercises
A 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 École 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
Audience:
R&D engineers and university researchers in image and signal processing; Signal processing and applied mathematics graduates
