Accelerating the pace of engineering and science

Wavelet Toolbox

Key Features

  • Continuous wavelet transform (CWT), scalogram, and wavelet coherence
  • Discrete wavelet analysis including decimated, nondecimated, dual-tree, and wavelet packet transforms
  • Signal and image denoising with scale-dependent or interval-dependent thresholding
  • Compression and reconstruction of signals and images including matching pursuit algorithms
  • Perfect reconstruction filter banks using coiflets, biorthogonal spline, Daubechies, and Fejer-Korovkin filters
  • Lifting method for constructing custom wavelets

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.

Continuous Wavelet Analysis

Wavelet Toolbox™ provides functions and apps to compute the continuous wavelet transform (CWT) of signals and images. You can analyze how the frequency content of a signal changes over time. You can also reconstruct time-frequency localized approximations of signals or filter out time-localized frequency components. Using wavelet coherence, you can reveal time-varying frequency content common in multiple signals. For images, continuous wavelet analysis shows how the frequency content of an image varies across the image and helps to reveal patterns in a noisy image.

Time-Frequency Analysis

Time-Frequency Analysis (Example)
Analyze signals jointly in time and frequency with the CWT.

Time-Varying Coherence

Time-Varying Coherence (Example)
Reveal time-varying patterns common in two signals.

Remove Time-Localized Frequency Components

Remove Time-Localized Frequency Components (Example)
Decompose a signal and reconstruct only selected components.

Detecting Self-Similarity

Detecting Self-Similarity (Example)
Study the fractal properties of signals with wavelets.

Continuous and Discrete Wavelet Analysis

Continuous and Discrete Wavelet Analysis (Example)
Explore the difference between the discrete and continuous wavelet transforms.

Pattern Adapted Wavelets for Signal Detection

Pattern Adapted Wavelets for Signal Detection (Example)
Detect and localize patterns in signals.

Discrete Wavelet Analysis

Wavelet Toolbox provides functions and apps to analyze signals and images into progressively finer octave bands using decimated (downsampled) and nondecimated wavelet transforms, including the maximal overlap discrete wavelet transform (MODWT). It also supports wavelet packet transforms that partition the frequency content of signals and images into progressively finer equal-width intervals.

This multiresolution analysis enables you to detect patterns that are not visible in the raw data. For example, you can measure the multiscale correlation between two signals or obtain multiscale variance estimates of signals to detect changepoints. You can also reconstruct signal and image approximations that retain only desired features, and compare the distribution of energy in signals across frequency bands. Use the wavelet packet spectrum to obtain a time-frequency analysis of a signal.

Wavelet Changepoint Detection

Wavelet Changepoint Detection (Example)
Detect changes in the variance of a signal using the MODWT.

Scale-Localized Volatility and Correlation

Scale-Localized Volatility and Correlation (Example)
Analyze signal variance and correlation over different time horizons with the MODWT.

Detect Discontinuities and Breakdown Points

Detect Discontinuities and Breakdown Points (Example)
Find edges and anomalies in signals and images with the discrete wavelet transform (DWT).

Wavelet Packets: Decomposing the Details

Wavelet Packets: Decomposing the Details (Example)
Decompose, denoise, and compress signals with wavelet packets.

Dual-Tree Wavelet Transforms

Dual-Tree Wavelet Transforms (Example)
Compare the dual-tree DWT with the standard DWT.

3D Wavelet Analysis

3D Wavelet Analysis (Example)
Approximate 3D MRI images with less than 1% of the original data.

Denoising and Compression

Wavelet Toolbox provides functions to denoise and compress signals and images. Wavelet and wavelet packet denoising enable you to retain features in your data that are often removed or smoothed out by other denoising techniques. Wavelet Toolbox supports a variety of thresholding strategies you can apply to your data and use to compare results. Noise in a signal is not always uniform in time, so you can apply interval-dependent thresholds to denoise data with nonconstant variance.

You can denoise and compress collections of signals with wavelets by exploiting correlations between individual signals. You can also cluster groups of signals by filtering out unimportant details using sparse wavelet representations. You can compress data by setting perceptually unimportant wavelet and wavelet packet coefficients to zero and reconstructing the data. The toolbox offers the Wavelet Design and Analysis app, which you can use to explore denoising and compressing signals and images.

Image Denoising

Image Denoising (Example)
Denoise images while preserving sharp edges.

Adaptive Signal Denoising

Adaptive Signal Denoising (Example)
Remove noise with nonconstant variance using interval-dependent thresholds.

Image Compression

Image Compression (Example)
Compress images while maintaining good visual quality.

Denoising Signals Using Matching Pursuit

Denoising Signals Using Matching Pursuit (Example)
Denoise signals while keeping transient, abrupt changes.

Multiscale Principal Components Analysis

Multiscale Principal Components Analysis (Example)
Denoise multivariate signals by exploiting correlations between the signals at multiple scales.

Signal Compression

Signal Compression (Example)
Reduce memory footprint to only 15% of original signal.

Filter Banks

Wavelet Toolbox provides functions that enable you to use the most common orthogonal and biorthogonal wavelet filters, including: Daubechies, coiflets, Fejer-Korovkin, and biorthogonal spline filters. Filter banks are arrangements of lowpass, highpass, and bandpass filters that divide your data into subbands that you can process independently. The orthogonal and biorthogonal wavelet filters the toolbox provides are specially designed to decompose your data, enabling you to operate on the subbands at different rates. These filters can also reconstruct the data while cancelling any aliasing errors that occur. Orthogonal wavelet filters accomplish this with a single filter pair. Biorthogonal filters require two pairs of filters but have the added benefit of providing linear phase. In both cases, the wavelet filters have very few coefficients and therefore provide you with a computationally efficient, perfect reconstruction filter bank.

The toolbox offers functions to design your own perfect reconstruction filter bank with specific properties through elementary lifting steps. You can also add your own custom wavelet filters and use them with the toolbox functions and apps for discrete and continuous wavelet analysis.

Orthogonal and Biorthogonal Filter Banks

Orthogonal and Biorthogonal Filter Banks (Example)
Compare orthogonal and biorthogonal filter banks.

Lifting a Filter Bank

Lifting a Filter Bank (Example)
Design a perfect reconstruction filter bank with specific properties.

Adding a Quadrature Mirror Filter

Adding a Quadrature Mirror Filter (Example)
Customize discrete wavelet or wavelet packet algorithms with your own quadrature mirror filter (QMF).

See how MATLAB and Simulink products fit within your application

Learn more