Wavelet Toolbox™ 4.2

Product Description

• Introduction and Key Features
• Applying Wavelet Methods
• Analyzing Signals and Images

Introduction

Wavelet Toolbox extends the MATLAB technical computing environment with graphical tools and command-line functions for developing wavelet-based algorithms for the analysis, synthesis, denoising, and compression of signals and images. Wavelet analysis provides more precise information about signal data than other signal analysis techniques, such as Fourier.

Wavelet Toolbox supports the interactive exploration of wavelet properties and applications. It is useful for speech and audio processing, image and video processing, biomedical imaging, and one-dimensional (1-D) and two-dimensional (2-D) applications in communications and geophysics.

Wavelet Toolbox authors are Michel Misiti, École Centrale de Lyon; Georges Oppenheim, Université de Marne-La-Vallée; Jean-Michel Poggi, Université René Descartes, Paris 5 Université; and Yves Misiti, Université Paris-Sud.

Key Features

• Standard wavelet families, including Daubechies wavelet filters, complex Morlet and Gaussian, real reverse biorthogonal, and discrete Meyer
• Wavelet and signal processing utilities, including a function to convert scale to frequency
• Methods for adding wavelet families
• Lifting methods for constructing wavelets
• Customizable presentation and visualization of data
• Interactive tools for continuous and discrete wavelet analysis
• Wavelet packets, implemented as MATLAB objects
• One-dimensional multisignal analysis, compression, and denoising
• Multiscale principal component analysis
• Multivariate denoising