from the book

You can download here the figures of the book. Simply click on the figure to retrieve a pdf file with the caption.

You can also retrieve all the figure as a single zip file.

Figures from chapters 1 to 11 can be reproduced using the Wavelab Matlab toolbox. The Wavelab directory has a folder called WaveTour. It contains a subdirectory for each chapter WTCh01, WTCh02, ...) ; these subdirectories include all the files needed to reproduce the computational figures from chapters 1 to 11. Each directory has a demo file.

Many of the figures of the books (including most of the numerical experiments of chapters 12 and 13) can be obtained by going through the numerical tours.

Fig.1.1: Linear vs. non-linear approximation

Fig.1.2: Denoising by wavelet thresholding

Fig.1.3: Heisenberg box representing a Gabor atom

Fig.1.4: Time-frequency boxes representing the energy spread of two windowed Fourier atoms.

Fig.1.5: Heisenberg time-frequency boxes of two wavelets

Fig.1.6: The time-frequency boxes of a wavelet basis define a tiling of the time-frequency plane

Fig.1.7: A wavelet packet basis divides the frequency axis in separate intervals of varying sizes.

Fig.1.8: A local cosine basis divides the time axis with smooth windows

Fig.10.1: Prefix tree

Fig.10.2: Coding tree

Fig.10.3: Huffman tree

Fig.10.4: Adapted windows

Fig.10.5: Wavelet packet tree

Fig.10.6: Audio coding

Fig.10.7: Wavelet compression with adaptive arithmetic coding

Fig.10.8: Rate distortion for image coding

Fig.10.9: Histogram for wavelet coefficients

Fig.10.10: Significance map

Fig.10.11: Rate distortion for wavelet coding

Fig.10.12: SPIHT embedded code

Fig.10.13: Block of cosine coefficients

Fig.10.14: JPEG compression

Fig.10.15: JPEG-2000 compression

Fig.10.16: JPEG-2000 process

Fig.10.17: JPEG-2000 ordering

Fig.11.1: Gaussian process denoising

Fig.11.2: Piecewise smooth signal Wiener denoising

Fig.11.3: Bayes vs. minimax

Fig.11.4: Piewise smooth wavelet denoising.

Fig.11.5: Sure denoising

Fig.11.6: Curvelet denoising

Fig.11.7: Audio denoising

Fig.11.8: Audio block denoising

Fig.11.9: Audio block denoising

Fig.11.10: Orthosymmetric set

Fig.12.1: Wavelet packet tree

Fig.12.2: Best wavelet packet basis

Fig.12.3: Time frequency distribution of a signal

Fig.12.4: Best cosine basis

Fig.12.5: Best cosine basis for an image

Fig.12.6: Wavelet coefficients as samples of a regularized function

Fig.12.7: Bandlet quadtree

Fig.12.8: Wavelet coefficient for bandletization

Fig.12.9: Discrete bandlets

Fig.12.10: Construction of a dyadic segmentation

Fig.12.11: Bandlet approximation

Fig.12.12: Bandlet denoising

Fig.12.13: Matching and basis pursuit in wavelet packet dictionary

Fig.12.14: Matching pursuit for audio signal

Fig.12.15: Directional Gabor wavelet pursuit

Fig.12.16: Video coding

Fig.12.17: Correlation decay during matching pursuit

Fig.12.18: Coherent structures by pursuit

Fig.12.19: Marching vs. basis pursuit in Gabor

Fig.12.20: Geometry of L1 minimization

Fig.12.21: Denoising of an image in a redundant dictionary

Fig.12.22: Unit balls

Fig.12.23: Total variation regularization

Fig.12.24: Image separation

Fig.12.25: Dictionary learning

Fig.13.1: Mirror wavelets

Fig.13.2: Satellite image deblurring

Fig.13.3: Sparse spikes deconvolution

Fig.13.4: Inpainting using wavelet regularization

Fig.13.5: Sparse superresolution

Fig.13.6: Super resolution by directional interpolation

Fig.13.7: Tomography inversion

Fig.13.8: Sparse spikes filters

Fig.13.9: Seismic filters

Fig.13.10: Compressed sensing recovery of sparse signals

Fig.13.11: Compressed sensing recovery of compressible signals

Fig.13.12: Blind source separation geometry

Fig.13.13: Blind source separation

Fig.2.1: Gibbs oscillations

Fig.2.2: Total variation.

Fig.2.3: Radon transform

Fig.3.1: Shannon Theorem

Fig.3.2: Aliasing

Fig.3.3: Periodization

Fig.4.1: Heisenberg box of a Gabor atoms

Fig.4.2: Heisenberg boxes of two Gabor atoms

Fig.4.3: Linear and quadratic shirps and modulated Gaussian

Fig.4.4: Window design

Fig.4.5: Four windows

Fig.4.6: Mexican hat

Fig.4.7: Mexican hat wavelet transform

Fig.4.8: Scaling function of mexican hat wavelet

Fig.4.9: Heisenberg boxes of two wavelets

Fig.4.10: Fourier transform of a wavelet

Fig.4.11: Scalogram

Fig.4.12: Ridges of a chirp

Fig.4.13: Ridges of chirps

Fig.4.14: Sum of two hyperbolic chirps

Fig.4.15: Ridge support computed from scalogram

Fig.4.16: Scalogram of two parallel linear chirps

Fig.4.17: Scalogram of two hyperbolic chirps

Fig.4.18: Wigner-Ville distribution of two Gabors

Fig.4.19: Wigner-Ville distribution of a signal

Fig.4.20: Choi-William distribution of two Gabor

Fig.4.21: Choi-William distribution of a signal

Fig.5.1: Fourier transform of wavelets

Fig.5.2: Dyadic wavelet transform

Fig.5.3: Quadratic spline wavelet and scaling function

Fig.5.4: Dyadic wavelet filters and reconstruction

Fig.5.5: Heisenberg box of a wavelet

Fig.5.6: Windowed Fourier frame

Fig.5.7: Musical recording

Fig.5.8: Oriented wavelets

Fig.5.9: Directional Gabor

Fig.5.10: Steerable pyramid

Fig.5.11: Curvelets and its Fourier transform

Fig.5.12: Spacial and Fourier tiling of curvelets

Fig.5.13: Layout of wavelets and curvelets around an edge

Fig.5.14: Curvelet frequency tiling.

Fig.6.1: Wavelet transform with derivated of Gaussigna.

Fig.6.2: Cone of singularities

Fig.6.3: Cone of oscillating singularity

Fig.6.4: Wavelet transform of a discontinuitiy

Fig.6.5: Modulus maxima

Fig.6.6: Decay of wavelet coefficient along maxima curves

Fig.6.7: Dyadic modulus maxima

Fig.6.8: Translation invariance of wavelets


Fig.6.10: Oriented wavelet transform and modulus maxima

Fig.6.11: Reconstruction from wavelet maxima

Fig.6.12: Restauration from thresholded maxima

Fig.6.13: Kaniza illusory contours

Fig.6.14: Von Koch subdivision

Fig.6.15: Cantor set

Fig.6.16: Cantor measure

Fig.6.17: Devil staircase

Fig.6.18: Concave spectrum

Fig.6.19: Devil staircase spectrum

Fig.6.20: Spectrum of a perturbation

Fig.6.21: Brownian motion

Fig.7.1: Cubic box spline

Fig.7.2: Cubic spline scaling function

Fig.7.3: Discrete multiresolution approximation

Fig.7.4: Cubic spline filters

Fig.7.5: Barrle-Lemarie cubic spline

Fig.7.6: Fourier transform of the Battle-Lemarie spline wavelets

Fig.7.7: Wavelet coefficients for cubic spline wavelets

Fig.7.8: Meyer Wavelets

Fig.7.9: Linear spline Battle-Lemarie

Fig.7.10: Daubechies scaling function

Fig.7.11: Daubechies and Symmlets scaling functions and wavelets

Fig.7.12: Fast wavelet transform

Fig.7.13: Wavelet reconstruction

Fig.7.14: Spline biorthogonal wavelets and scaling functions

Fig.7.15: Biorthogonal wavelets and scaling functions

Fig.7.16: Periodic wavelet

Fig.7.17: Folded signal

Fig.7.18: Boundary scaling function and wavelets

Fig.7.19: Cubic spline interpolation functions

Fig.7.20: Dual cubic spline for interpolation

Fig.7.21: Multiresolution image approximation

Fig.7.22: Fourier transform of a separable wavelet transform

Fig.7.23: Fourier support of 2D wavelets

Fig.7.24: Separable wavelet transform of Lena

Fig.7.25: Fast 2D wavelet transform

Fig.7.26: Predict and update lifting

Fig.7.27: Predict and update for linear wavelet lifting

Fig.7.28: Quincunx subsampling

Fig.7.29: Quincunx wavelets

Fig.7.30: Decomposition of an image in a biorthogonal quincunx wavelet

Fig.7.31: Triangular mesh subsampling

Fig.7.32: Example of semi-regular mesh

Fig.7.33: Labeling of points in butterfly scheme

Fig.7.34: Non-linear approximation of a function on the sphere

Fig.7.35: Dual wavelet on a triangles

Fig.7.36: Non-linear surface approximation

Fig.8.1: Binary wavelet packet tree

Fig.8.2: Wavelet packet with Daubechies filters

Fig.8.3: Admissible packet tree

Fig.8.4: Walsh packets

Fig.8.5: Heisenberg box of wavelet packets

Fig.8.6: Multi-chirp signal decomposed in wavelet packets

Fig.8.7: Dyadic wavelet basis tree.

Fig.8.8: Multi-chirp decomposition in cosine wavelet packet

Fig.8.9: Admissible tree and Heisenberg box

Fig.8.10: Wavelet packet analysis and synthesis

Fig.8.11: Wavelet packet quad tree

Fig.8.12: Wavelet packet frequency decomposition

Fig.8.13: Wavelet packet decomposition in 2D

Fig.8.14: Symmetric extension

Fig.8.15: Cosine IV

Fig.8.16: Lapper projector

Fig.8.17: Local cosine windows

Fig.8.18: Local DCT Heisenberg boxes

Fig.8.19: Local cosine audio decomposition

Fig.8.20: Local cosine admissible tree

Fig.8.21: Local cosine windows

Fig.8.22: Local cosine image subdivision

Fig.9.1: Fourier vs. Wavelets approximation

Fig.9.2: Non-linear wavelet approximation

Fig.9.3: Approximation curves

Fig.9.4: Linear vs. non-linear approximation

Fig.9.5: Approximation using triangulation

Fig.9.6: Adapted finite element approximation

Fig.9.7: Aspect ratio of triangles