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.
Learn the basics of Wavelet Toolbox
CWT, scalogram, wavelet coherence, wavelet cross-spectrum, real- and complex-valued wavelets
DWT, MODWT, dual-tree wavelet transform, wavelet packets, multisignal analysis
Wavelet shrinkage, nonparametric regression, block thresholding, multisignal thresholding
Orthogonal and biorthogonal wavelet and scaling filters, lifting
Generate C/C++ code and MEX functions for toolbox functions