The Sparse Way To order this title, and for more information, click here Third Edition
By Stephane Mallat, cole Polytechique, Centre de Math matiques Appliqu es, 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 cole 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
Contents Preface to the Sparse Edition
Notations
1. Sparse Representations
1.1 Computational Harmonic Analysis
1.1.1 Fourier Kingdom
1.1.2
Wavelet Bases
1.2 Approximation and Processing in Bases
1.2.1 Sampling with Linear Approximations
1.2.2 Sparse Non-linear Approximations
1.2.3 Compression
1.2.4 Denoising
1.3 Time-Frequency Dictionaries
1.3.1 Heisenberg Uncertainty
1.3.2 Windowed Fourier Transform
1.3.3 Continuous Wavelet Transform
1.3.4 Time-Frequency Orthonormal Bases
1.4 Sparsity in Redundant Dictionaries
1.4.1 Frame Analysis
and Synthesis
1.4.2 Ideal Dictionary Approximations
1.4.3 Pursuit in Dictionaries
1.5 Inverse Problems
1.5.1 Diagonal Inverse Estimation
1.5.2 Super-Resolution and Compressive Sensing
1.6 Travel Guide
2. Fourier Kingdom
2.1 Linear Time-Invariant Filtering
2.1.1 Impulse
Response
2.1.2 Transfer Functions
2.2 Fourier Integrals
2.2.1 Fourier Transform in L1(R)
2.2.2 Fourier Transform in L2(R)
2.2.3
Examples
2.3 Properties
2.3.1 Regularity and Decay
2.3.2 Uncertainty Principle
2.3.3 Total Variation
2.4 Two-Dimensional Fourier
Transform
2.5 Exercises
3. Discrete Revolution
3.1 Sampling Analog Signals
3.1.1 Shannon-Whittaker Sampling Theorem
3.1.2 Aliasing
3.1.3 General Sampling and Linear Analog Conversions
3.2 Discrete Time-Invariant Filters
3.2.1 Impulse Response and Transfer Function
3.2.2 Fourier Series
3.3 Finite Signals
3.3.1 Circular Convolutions
3.3.2 Discrete Fourier Transform
3.3.3 Fast Fourier Transform
3.3.4 Fast Convolutions
3.4 Discrete Image Processing
3.4.1 Two-Dimensional Sampling Theorems
3.4.2 Discrete Image Filtering
3.4.3
Circular Convolutions and Fourier Basis
3.5 Exercises
4 Time Meets Frequency
4.1 Time-Frequency Atoms
4.2 Windowed Fourier Transform
4.2.1 Completeness and Stability
4.2.2 Choice of Window
4.2.3 Discrete Windowed Fourier Transform
4.3 Wavelet Transforms
4.3.1 Real
Wavelets
4.3.2 Analytic Wavelets
4.3.3 Discrete Wavelets
4.4 Time-Frequency Geometry of Instantaneous Frequencies
4.4.1 Windowed Fourier
Ridges
4.4.2 Wavelet Ridges
4.5 Quadratic Time-Frequency Energy
4.5.1 Wigner-Ville Distribution
4.5.2 Interferences and Positivity
4.5.3 Cohen s Class
4.5.4 Discrete Wigner-Ville Computations
4.6 Exercises
5. Frames
5.1 Frames and Riesz Bases
5.1.1 Stable Analysis
and Synthesis Operators
5.1.2 Dual Frame and Pseudo Inverse
5.1.3 Dual Frame Analysis and Synthesis Computations
5.1.4 Frame Projector
and Reproducing Kernel
5.1.5 Translation Invariant Frames
5.2 Translation Invariant Dyadic Wavelet Transform
5.2.1 Dyadic Wavelet
Design
5.2.2 Algorithme `a Trous
5.3 Subsampled Wavelet Frames
5.4 Windowed Fourier Frames
5.5 Multiscale Directional Frames
for Images
5.5.1 Directional Wavelet Frames
5.5.2 Curvelet Frames
5.6 Exercises
6. Wavelet Zoom
6.1 Lipschitz Regularity
6.1.1 Lipschitz
Definition and Fourier Analysis
6.1.2 Wavelet Vanishing Moments
6.1.3 Regularity Measurements with Wavelets
6.2 Wavelet Transform
Modulus Maxima
6.2.1 Detection of Singularities
6.2.2 Dyadic Maxima Representation
6.3 Multiscale Edge Detection
6.3.1 Wavelet Maxima
for Images
6.3.2 Fast Multiscale Edge Computations
6.4 Multifractals
6.4.1 Fractal Sets and Self-Similar Functions
6.4.2 Singularity
Spectrum
6.4.3 Fractal Noises
6.5 Exercises
7. Wavelet Bases
7.1 Orthogonal Wavelet Bases
7.1.1 Multiresolution Approximations
7.1.2 Scaling Function
7.1.3 Conjugate Mirror Filters
7.1.4 In Which Orthogonal Wavelets Finally Arrive
7.2 Classes of Wavelet Bases
7.2.1 Choosing a Wavelet
7.2.2 Shannon, Meyer and Battle-Lemari e Wavelets
7.2.3 Daubechies Compactly Supported Wavelets
7.3 Wavelets
and Filter Banks
7.3.1 Fast Orthogonal Wavelet Transform
7.3.2 Perfect Reconstruction Filter Banks
7.3.3 Biorthogonal Bases of 2(Z)
7.4 Biorthogonal Wavelet Bases
7.4.1 Construction of Biorthogonal Wavelet Bases
7.4.2 Biorthogonal Wavelet Design
7.4.3 Compactly
Supported Biorthogonal Wavelets
7.5 Wavelet Bases on an Interval
7.5.1 Periodic Wavelets
7.5.2 Folded Wavelets
7.5.3 Boundary Wavelets
7.6 Multiscale Interpolations
7.6.1 Interpolation and Sampling Theorems
7.6.2 Interpolation Wavelet Basis
7.7 Separable Wavelet Bases
7.7.1 Separable Multiresolutions
7.7.2 Two-Dimensional Wavelet Bases
7.7.3 Fast Two-Dimensional Wavelet Transform
7.7.4 Wavelet Bases
in Higher Dimensions
7.8 Lifting Wavelets
7.8.1 Biorthogonal Bases over Non-stationary Grids
7.8.2 The Lifting Scheme
7.8.3 Quincunx
Wavelet Bases
7.8.4 Wavelets on Bounded Domains and Surfaces
7.8.5 Faster Wavelet Transform with Lifting
7.9 Exercises
8. Wavelet
Packet and Local Cosine Bases
8.1 Wavelet Packets
8.1.1 Wavelet Packet Tree
8.1.2 Time-Frequency Localization
8.1.3 Particular Wavelet
Packet Bases
8.1.4 Wavelet Packet Filter Banks
8.2 Image Wavelet Packets
8.2.1 Wavelet Packet Quad-Tree
8.2.2 Separable Filter Banks
8.3 Block Transforms
8.3.1 Block Bases
8.3.2 Cosine Bases
8.3.3 Discrete Cosine Bases
8.3.4 Fast Discrete Cosine Transforms
8.4
Lapped Orthogonal Transforms
8.4.1 Lapped Projectors
8.4.2 Lapped Orthogonal Bases
8.4.3 Local Cosine Bases
8.4.4 Discrete Lapped
Transforms
8.5 Local Cosine Trees
8.5.1 Binary Tree of Cosine Bases
8.5.2 Tree of Discrete Bases
8.5.3 Image Cosine Quad-Tree
8.6
Exercises
9. Approximations in Bases
9.1 Linear Approximations
9.1.1 Sampling and Approximation Error
9.1.2 Linear Fourier Approximations
.
9.1.3 Multiresolution Approximation Errors with Wavelets
9.1.4 Karhunen-Lo`eve Approximations
9.2 Non-Linear Approximations
9.2.1
Non-Linear Approximation Error
9.2.2 Wavelet Adaptive Grids
9.2.3 Approximations in Besov and Bounded Variation Spaces
9.3 Sparse
Image Representations
9.3.1 Wavelet Image Approximations
9.3.2 Geometric Image Models and Adaptive Triangulations
9.3.3 Curvelet Approximations
9.4 Exercises
10. Compression
10.1 Transform Coding
10.1.1 Compression State of the Art
10.1.2 Compression in Orthonormal Bases
10.2 Distortion Rate of Quantization
10.2.1 Entropy Coding
10.2.2 Scalar Quantization
10.3 High Bit Rate Compression
10.3.1 Bit
Allocation
10.3.2 Optimal Basis and Karhunen-Lo`eve
10.3.3 Transparent Audio Code
10.4 Sparse Signal Compression
10.4.1 Distortion
Rate and Wavelet Image Coding
10.4.2 Embedded Transform Coding
10.5 Image Compression Standards
10.5.1 JPEG Block Cosine Coding
10.5.2
JPEG-2000 Wavelet Coding
10.6 Exercises
11. Denoising
11.1 Estimation with Additive Noise
11.1.1 Bayes Estimation
11.1.2 Minimax
Estimation
11.2 Diagonal Estimation in a Basis
11.2.1 Diagonal Estimation with Oracles
11.2.2 Thresholding Estimation
11.2.3 Thresholding
Refinements
11.2.4 Wavelet Thresholding
11.2.5 Wavelet and Curvelet Image Denoising
11.2.6 Audio Denoising by Time-Frequency Thresholding
11.3 Non-Diagonal Block Thresholding
11.3.1 Block Thresholding in Bases and Frames
11.3.2 Wavelet Block Thresholding
11.3.3 Time-Frequency
Audio Block Thresholding
11.4 Denoising Minimax Optimality
11.4.1 Linear Diagonal Minimax Estimation
11.4.2 Orthosymmetric Sets
11.4.3
Nearly Minimax with Wavelet Thresholding
11.5 Exercises
12. Sparse in Redundant Dictionaries
12.1 Ideal Sparse Processing in Dictionaries
12.1.1 Best Approximation
12.1.2 Compression by Support Coding in a Dictionary
12.1.3 Denoising in a Dictionary
12.2 Dictionaries
of Orthonormal Bases
12.2.1 Approximation, Compression and Denoising in a Best Basis
12.2.2 Fast Best Basis Search in Tree Dictionaries
12.2.3 Wavelet Packet and Local Cosine Best Bases
12.2.4 Bandlet Dictionaries for Geometric Processing
12.3 Greedy Pursuits
12.3.1
Matching Pursuit
12.3.2 Orthogonal Matching Pursuit .
12.3.3 Gabor Dictionaries
12.3.4 Learning Dictionaries
12.3.5 Coherent Matching
Pursuit Denoising
12.4 l1 Pursuits
12.4.1 Basis Pursuit
12.4.2 l1 Lagrangian Pursuit
12.5 Approximation Performance of Pursuits
12.5.1
Support Identification and Stability
12.5.2 Support Dependent Success of Pursuits
12.5.3 Sparsity Dependent Criterions and Mutual-Coherence
12.6 Inverse Problems
12.6.1 Linear Estimation and Singular Value Decompositions
12.6.2 Thresholding Inverse Problem Estimators
12.6.3
Super-Resolution
12.6.4 Compressive Sensing
12.6.5 Source Separation
12.7 Exercises
A. Mathematical Complements
A.1 Functions and
Integration
A.2 Banach and Hilbert Spaces
A.3 Bases of Hilbert Spaces
A.4 Linear Operators
A.5 Separable Spaces and Bases
A.6 Random
Vectors and Covariance Operators
A.7 Diracs
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