Wavelet Toolbox
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 t波ransform, 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