Product Description

This book is intended to serve as an invaluable reference for anyone concerned with the application of wavelets to signal processing. It has evolved from material used to teach "wavelet signal processing" courses in electrical engineering departments at Massachusetts Institute of Technology and Tel Aviv University, as well as applied mathematics departments at the Courant Institute of New York University and École
Polytechnique in Paris.

Key Features
* Provides a broad perspective on the principles and applications of transient signal processing with wavelets
* Emphasizes intuitive understanding, while providing the mathematical foundations and description of fast algorithms
* Numerous examples of real applications to noise removal, deconvolution, audio and image compression, singularity and edge detection,
multifractal analysis, and time-varying frequency measurements
* Algorithms and numerical examples are implemented in Wavelab, which is a Matlab toolbox freely available over the Internet
* Content is accessible on several level of complexity, depending on the individual reader's needs
New to the Second Edition
* Optical flow calculation and video compression algorithms
* Image models with bounded variation functions
* Bayes and Minimax theories for signal estimation
* 200 pages rewritten and most illustrations redrawn
* More problems and topics for a graduate course in wavelet signal processing, in engineering and applied mathematics

Product Details

• Amazon Sales Rank: #749163 in Books
• Published on: 1999-09-17
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 620 pages

Review
"Mallat has not only written a treatise, but also an excellent graduate
text for students in computer science, electrical engineering, and
mathematics" John J. Benedetto, SIAM review -- Review

Review
"Mallat has not only written a treatise, but also an excellent graduate
text for students in computer science, electrical engineering, and
mathematics" John J. Benedetto, SIAM review

From the Back Cover
This book is intended to serve as an invaluable reference for anyone concerned with the application of wavelets to signal processing. It has evolved from material used to teach "wavelet signal processing" courses in electrical engineering departments at Massachusetts Institute of Technology and Tel Aviv University, as well as applied mathematics departments at the Courant Institute of New York University and École
Polytechnique in Paris.

*Provides a broad perspective on the principles and applications of transient signal processing with wavelets.
*Emphasizes intuitive understanding, while providing the mathematical foundations and description of fast algorithms.
*Numerous examples of real applications to noise removal, deconvolution, audio and image compression, singularity and edge detection,
multifractal analysis, and time-varying frequency measurements.
*Algorithms and numerical examples are implemented in Wavelab, which is a Matlab toolbox freely available over the Internet.
*Content is accessible on several level of complexity, depending on the individual reader's needs.
*Reviews Fourier analysis and elementary signal processing.
*Introduces windowed Fourier transforms, continuous wavelet transforms, and Wigner-Ville transforms.
*Explains the construction of frames, wavelet orthogonal and biorthogonal bases, wavelet packet and local cosine bases.
*Covers basic approximation theory with applications to signal estimation and transform coding.

Customer Reviews

A bold approach to wavelet transforms that simplifies
This is an outstanding tour through the field of wavelet decompositions of both continuous and discrete signals. It employs the formalism of Hilbert space, instead of linear algebra. This is important because the power of this formalism yields insights into the subject matter that are practically impossible in linear algebra. The formalized approach allows a wide variety of subjects to be placed on a common basis (no pun intended). For example, the transition of the treatment of the Fourier transform into Hilbert space, brings to bear the powerful guns of that space (such guns as inner product and completeness), and allows for a truly elegant proof of the Parseval and Plancherel formulas.
Parseval's theorem, simply stated, is that the inner products in Hilbert space are conserved by the Fourier transform. How simple. Linear algebra approaches cannot hope to make things this simple.

Proof of the General Sampling Theorem is equally elegant; it is shown that the projection of the function to be decomposed onto a basis function gives the discrete spectral coefficient.

Readers will also enjoy the treatment of windowed Fourier transforms and frames.

I should add a note about the style of the treatise. This treatise is not ordinary. It consistently uses very precise and carefully defined symbology. Contrary to popular belief, this makes the text easier to read, not more difficult. Once the reader understands the symbol set being used (they are all defined in the front of the text), even the proofs are tractable. Yes, I said proofs. That is another aspect of the text. There are proofs embedded in the text, without loss of continuity or clarity. Proofs are necessary to a good understanding of the subject matter. The formalism of theorems, lemmas and propositions makes the conclusions understandable, because the theorems, lemmas and propositions supporting the conclusions are identifiable.

I applaud the author for his approach and recommend that other text book writers use the same approach.

Searching for an understanding of wavelet concepts ?
As someone learning about wavelets on their own, I found this book much more approachable than many others. You don't need to chew through the proofs to understand the concepts - and if you do, they're ranked according to difficulty ! The chapters are relatively self-contained, and just when your mind begins to stray he throws in a really interesting example.

GREAT and CLEAR
I bought this book because of the good name of the author at first. He used great and clear Mathematics and diagrams to explain the theory and applications of the wavelet. It is easy for graduate student to follow, I feel. And I kept this book as my faviour book in my bookself.