The type of wavelet analysis best suited for your work depends on what you want to do with the data. This topic focuses on 1D data, but you can apply the same principles to 2D data.
If your goal is to perform a detailed timefrequency analysis, choose the continuous wavelet transform (CWT).
The CWT is superior to the shorttime Fourier transform (STFT) for signals in which the instantaneous frequency grows rapidly, such as in a hyperbolic chirp.
The CWT is good at localizing transients in nonstationary signals.
In terms of implementation, scales are discretized more finely in the CWT than in the discrete wavelet transform (DWT). See Continuous and Discrete Wavelet Transforms for more details.
To obtain the continuous wavelet transform of your data, use the cwt
function. You can use the
wname
argument of this function to specify the type of
wavelet best suited for your data. By default, cwt
uses the
generalized Morse wavelet family. This family is defined by two parameters. You can
vary the parameters to recreate many commonly used wavelets.
Wavelet  Features  wname 

Generalized Morse Wavelet  Can vary two parameters to change time and frequency spread  'morse' (default) 
Analytic Morlet Wavelet  Provides good time localization  'amor' 
Bump Wavelet  Provides good frequency localization  'bump' 
All the wavelets in the table are analytic. Analytic wavelets are
wavelets with onesided spectra, and are complex valued in the time domain. These
wavelets are a good choice for obtaining a timefrequency analysis using the CWT.
Because the wavelet coefficients are complex valued, the CWT provides phase
information. cwt
supports analytic and antianalytic wavelets.
See TimeFrequency Analysis with the Continuous Wavelet Transform for additional
information.
If you want to obtain a multiresolution analysis, or if you are working with a sparse representation of the data, then choose the discrete wavelet transform (DWT).
If preserving energy in the analysis stage is important, you must use an
orthogonal wavelet. An orthogonal transform preserves energy. Consider using an
orthogonal wavelet with compact support. Keep in mind that except for the Haar
wavelet, orthogonal wavelets with compact support are not symmetric. The associated
filters have nonlinear phase. This table lists supported orthogonal wavelets. You
can use the wname
argument in all the discrete wavelet transform
functions to specify the type of wavelet best suited for your data. See
wavemngr('read')
for all wavelet family names.
Orthogonal Wavelet  Features  wname  See Also 

Coiflet  Scaling function and wavelets have same number of vanishing moments  'coifN' for N = 1, 2, ...,
5  N/A 
Daubechies  Nonlinear phase; energy concentrated near the start of their support  'dbN' for N = 1, 2, ...,
45  dbaux , Extremal Phase Wavelet Coefficients 
FejérKorovkin  Filters constructed to minimize the difference between a valid scaling filter and the ideal sinc lowpass filter; are especially useful in discrete (decimated and undecimated) wavelet packet transforms.  'fkN' for N = 4, 6, 8, 14, 18,
22  N/A 
Haar  Symmetric; special case of Daubechies; useful for edge detection  'haar' ('db1' )  N/A 
Symlet  Least asymmetric; nearly linear phase  'symN' for N = 2, 3, ...,
45  symaux , Symlets and Phase 
Use waveinfo
to learn more about
individual wavelet families. For example, waveinfo('db')
.
Depending on how you address border distortions, the DWT might not conserve energy
in the analysis stage. However, the maximal overlap DWT (modwt
) does conserve energy. See
dwtmode
and Border Effects for more information.
If you want to find closely spaced features, choose wavelets with smaller support,
such as haar
, db2
, or sym2
.
The support of the wavelet should be small enough to separate the features of
interest. Wavelets with larger support tend to have difficulty detecting closely
spaced features. Using wavelets with large support can result in coefficients that
do not distinguish individual features. For an example, see Effect of Wavelet Support on Noisy Data. If your data
has sparsely spaced transients, you can use wavelets with larger support.
If your goal is to conduct an analysis of variance, the maximal overlap discrete wavelet transform (MODWT) is suited for the task. The MODWT is a variation of the standard DWT.
The MODWT conserves energy in the analysis stage.
The MODWT partitions variance across scales. For examples, see Wavelet Analysis of Financial Data and Wavelet Changepoint Detection.
The MODWT requires an orthogonal wavelet, such as a Daubechies wavelet or symlet.
The MODWT is a shiftinvariant transform. Shifting the input data does not change the wavelet coefficients. The coefficients are shifted as well. The decimated DWT is not shift invariant. Shifting the input changes the coefficients and can redistribute energy across scales.
See modwt
and modwtmra
for more information. See
also Comparing MODWT and MODWTMRA.
If your work requires representing a signal with minimal redundancy, use the DWT. If your work requires a redundant representation, use the MODWT. For an example, see Continuous and Discrete Wavelet Analysis of Frequency Break.
An orthogonal wavelet, such as a symlet or Daubechies wavelet, is a good choice for denoising signals. A biorthogonal wavelet can also be good for denoising images.
An orthogonal transform does not color white noise. If white noise is provided as input to an orthogonal transform, the output is white noise. Performing a DWT with a biorthogonal wavelet colors white noise.
An orthogonal transform preserves energy.
To learn if a wavelet family is orthogonal, use waveinfo
. For example,
waveinfo('sym')
.
The sym4
wavelet is the default wavelet used in the wdenoise
function and the Wavelet Signal
Denoiser app.
If your work involves signal or image compression, consider using a biorthogonal
wavelet. This table lists the supported biorthogonal wavelets with compact support. You
can use the wname
argument in all the discrete wavelet transform
functions to specify the biorthogonal wavelet best suited for your data.
Biorthogonal Wavelet  Features  wname 

Biorthogonal Spline  Compact support; symmetric filters; linear phase  'biorNr.Nd' where Nr and
Nd are the numbers of vanishing moments for
the reconstruction and decomposition filters, respectively; see
waveinfo('bior') for supported values

Reverse Biorthogonal Spline  Compact support; symmetric filters; linear phase  'rbioNd.Nr' where Nr and
Nd are the numbers of vanishing moments for
the reconstruction and decomposition filters, respectively; see
waveinfo('rbio') for supported values 
Having two scaling functionwavelet pairs, one pair for analysis and another for synthesis, is useful for compression.
The filters are symmetric and have linear phase.
The wavelets used for analysis can have many vanishing moments. A wavelet with
N
vanishing moments is orthogonal to polynomials of
degree N1
. Using a wavelet with many vanishing moments
results in fewer significant wavelet coefficients. Compression is
improved.
The dual wavelets used for synthesis can have better regularity. The reconstructed signal is smoother.
Using an analysis filter with fewer vanishing moments than a synthesis filter can adversely affect compression. For an example, see Image Reconstruction with Biorthogonal Wavelets.
When using biorthogonal wavelets, energy is not conserved at the analysis stage. See Orthogonal and Biorthogonal Filter Banks for additional information.
Wavelets have properties that govern their behavior. Depending on what you want to do, some properties can be more important.
If a wavelet is orthogonal, the wavelet transform preserves energy. Except for the Haar wavelet, no orthogonal wavelet with compact support is symmetric. The associated filter has nonlinear phase.
A wavelet with N
vanishing moments is orthogonal to polynomials
of degree N1
. For an example, see Wavelets and Vanishing Moments. The number of
vanishing moments and the oscillation of the wavelet have a loose relationship. The
greater number of vanishing moments a wavelet has, the more the wavelet
oscillates.
Names for many wavelets are derived from the number of vanishing moments. For
example, db6
is the Daubechies wavelet with six vanishing moments
and sym3
is the symlet with three vanishing moments. For coiflet
wavelets, coif3
is the coiflet with six vanishing moments. For
FejérKorovkin wavelets, fk8
is the FejérKorovkin wavelet with a
length 8 filter. Biorthogonal wavelet names are derived from the number of vanishing
moments the analysis wavelet and synthesis wavelet each have. For instance,
bior3.5
is the biorthogonal wavelet with three vanishing
moments in the synthesis wavelet and five vanishing moments in the analysis wavelet.
To learn more, see waveinfo
and wavemngr
.
The number of vanishing moments also affects the support of a wavelet. Daubechies
proved that a wavelet with N
vanishing moments must have a
support of at least length 2N1
.
Regularity is related to how many continuous derivatives a function has.
Intuitively, regularity can be considered a measure of smoothness. To detect an
abrupt change in the data, a wavelet must be sufficiently regular. For a wavelet to
have N
continuous derivatives, the wavelet must have at least
N+1
vanishing moments. See Detecting Discontinuities and Breakdown Points for an example. If your data
is relatively smooth with few transients, a more regular wavelet might be a better
fit for your work.
Wavelet Signal
Denoiser  dwtmode
 waveinfo
 wavemngr
 wdenoise