This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materials including this page, select Japan from the country navigator on the bottom of this page.

Wavelet Toolbox

Analyze and synthesize signals and images using wavelets

Wavelet Toolbox™ provides functions and apps for analyzing and synthesizing signals, images, and data that exhibit regular behavior punctuated with abrupt changes. The toolbox includes algorithms for continuous wavelet transform (CWT), scalogram, and wavelet coherence. It also provides algorithms and visualizations for discrete wavelet analysis, including decimated, nondecimated, dual-tree, and wavelet packet transforms. In addition, you can extend the toolbox algorithms with custom wavelets.

The toolbox lets you analyze how the frequency content of signals changes over time and reveals time-varying patterns common in multiple signals. You can perform multiresolution analysis to extract fine-scale or large-scale features, identify discontinuities, and detect change points or events that are not visible in the raw data. You can also use Wavelet Toolbox to efficiently compress data while maintaining perceptual quality and to denoise signals and images while retaining features that are often smoothed out by other techniques.

Getting Started

Learn the basics of Wavelet Toolbox

Continuous Wavelet Analysis

CWT, scalogram, wavelet coherence, wavelet cross-spectrum, real- and complex-valued wavelets

Discrete Wavelet Analysis

DWT, MODWT, dual-tree wavelet transform, wavelet packets, multisignal analysis

Denoising and Compression

Wavelet shrinkage, nonparametric regression, block thresholding, multisignal thresholding

Filter Banks

Orthogonal and biorthogonal wavelet and scaling filters, lifting

Code Generation

Generate C/C++ code and MEX functions for toolbox functions

Was this topic helpful?