A Wavelet Tour of Signal Processing

Welcome to the home page of the Wavelet Tour book

This web page gathers materials to complement the third edition of the book A Wavelet Tour of Signal Processing, 3rd edition, The Sparse Way, of Stéphane Mallat. In particular you can download all the figures from the book and perform numerical experiments using Matlab, Scilab or Python. Solutions of problems from the book can also be obtained.

Getting the book

You can buy the book from your favorite bookstore, for instance Amazon or Barnes and Noble. See also the page of the editor.

Download the first chapter

You can download PDF files of the Preface/Table of content and the first chapter of the book.

About the Third Edition

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.

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. Stéphane Mallat is Professor in Applied Mathematics at ENS, 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.


  • Balances presentation of the mathematics with applications to signal processing.
  • Algorithms and numerical examples are implemented in MATLAB.
  • 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.