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.

Analyze signals jointly in time and frequency with the CWT.
Reveal time-varying patterns common in two signals.
Decompose a signal and reconstruct only selected components.
Study the fractal properties of signals with wavelets.
Explore the difference between the discrete and continuous wavelet transforms.
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.

Detect changes in the variance of a signal using the MODWT.
Analyze signal variance and correlation over different time horizons with the MODWT.
Find edges and anomalies in signals and images with the discrete wavelet transform (DWT).
Decompose, denoise, and compress signals with wavelet packets.
Compare the dual-tree DWT with the standard DWT.
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.

Denoise images while preserving sharp edges.
Remove noise with nonconstant variance using interval-dependent thresholds.
Compress images while maintaining good visual quality.
Denoise signals while keeping transient, abrupt changes.
Denoise multivariate signals by exploiting correlations between the signals at multiple scales.
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.

Compare orthogonal and biorthogonal filter banks.
Design a perfect reconstruction filter bank with specific properties.
Customize discrete wavelet or wavelet packet algorithms with your own quadrature mirror filter (QMF).