Synopsis
This volume begins with a presentation of Fourier transform and then describes its failures for transient signal processing. It presents local time-frequency methods and the related mathematical tools. The book uses an intuitive approach to important mathematical results, and emphasizes practical applications rather than proofs. It describes numerical discreet algorithms as well as some applications to information processing, fractal analysis, noise removal, and compact signal coding. This work is intended for signal processing engineers who want to discover the potential applications of recent mathematical advances in time-frequency signal representations. Also of interest to researchers in applied mathematics, the book highlights the applications of these new techniques and also provides an overview of signal processing problems.
Présentation de l'éditeur
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
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.