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A Wavelet Tour of Signal Processing
ID: 170002
Stephane Mallat
Mallat's book is the undisputed reference in this bread. - Laurent Demanet, Stanford University
The new edition of the classic book is a reflection of the key concept. 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 written in redundant dictionaries, super-resolution and compressive sensing applications.
Features:
* Balances presentation of mathematics with applications to signal processing
* Algorithms and numerical examples are implemented in WaveLab, and MATLAB toolbox
* Companion website for instructors and selected solutions 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 surfaces
* Time-frequency audio processing and denoising
* Image compression with JPEG-2000
* New and updated exercises
A Wavelet Tour of Signal Processing: R & D engineers wishing to apply the theory in fields, 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 the CEO of an image processing semiconductor company.
Companion website: A Numerical Tour of Signal Processing
Includes all the latest developments
application to JPEG 2000 and MPEG-4
Algorithms and numerical examples are implemented in Wavelab, and MATLAB toolbox
Balances presentation of the mathematics with applications to signal processing
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
The new edition of the classic book is a reflection of the key concept. 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 written in redundant dictionaries, super-resolution and compressive sensing applications.
Features:
* Balances presentation of mathematics with applications to signal processing
* Algorithms and numerical examples are implemented in WaveLab, and MATLAB toolbox
* Companion website for instructors and selected solutions 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 surfaces
* Time-frequency audio processing and denoising
* Image compression with JPEG-2000
* New and updated exercises
A Wavelet Tour of Signal Processing: R & D engineers wishing to apply the theory in fields, 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 the CEO of an image processing semiconductor company.
Companion website: A Numerical Tour of Signal Processing
application to JPEG 2000 and MPEG-4
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
170002
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