BUILT-IN MATHEMATICA SYMBOL

StationaryWaveletTransform

gives the stationary wavelet transform (SWT) of an array of data.

StationaryWaveletTransform[data, wave]
gives the stationary wavelet transform using the wavelet wave.

StationaryWaveletTransform[data, wave, r]
gives the stationary wavelet transform using r levels of refinement.

StationaryWaveletTransform[image, ...]
gives the stationary wavelet transform of an image.

StationaryWaveletTransform[sound, ...]
gives the stationary wavelet transform of sampled sound.

Details and OptionsDetails and Options

• StationaryWaveletTransform gives a DiscreteWaveletData object.
• Properties of the DiscreteWaveletData dwd can be found using dwd["prop"], and a list of available properties can be found using dwd["Properties"].
• StationaryWaveletTransform is similar to DiscreteWaveletTransform except that no subsampling occurs at any refinement level and the resulting coefficient arrays all have the same dimensions as the original data.
• The data can be a rectangular array of any depth.
• By default, input image is converted to an image of type .
• The resulting wavelet coefficients are arrays of the same depth and dimensions as the input data.
• The possible wavelets wave include:
•  BattleLemarieWavelet[...] Battle-Lemarié wavelets based on B-spline BiorthogonalSplineWavelet[...] B-spline-based wavelet CoifletWavelet[...] symmetric variant of Daubechies wavelets DaubechiesWavelet[...] the Daubechies wavelets HaarWavelet[...] classic Haar wavelet MeyerWavelet[...] wavelet defined in the frequency domain ReverseBiorthogonalSplineWavelet[...] B-spline-based wavelet (reverse dual and primal) ShannonWavelet[...] sinc function-based wavelet SymletWavelet[...] least asymmetric orthogonal wavelet
• The default wave is .
• With higher settings for the refinement level r, larger-scale features are resolved.
• The default refinement level r is given by , where is the minimum dimension of data.  »
• The tree of wavelet coefficients at level consists of coarse coefficients and detail coefficients , with representing the input data.
• The forward transform is given by and , where is the filter length for the corresponding wspec and is the length of input data.  »
• The inverse transform is given by .  »
• The are lowpass filter coefficients and are highpass filter coefficients that are defined for each wavelet family.
• The dimensions of and are the same as input data dimensions.
• The following options can be given:
•  Method Automatic method to use WorkingPrecision MachinePrecision precision to use in internal computations
• StationaryWaveletTransform uses periodic padding of data.
• InverseWaveletTransform gives the inverse transform.

ExamplesExamplesopen allclose all

Basic Examples (3)Basic Examples (3)

Compute a stationary wavelet transform using the HaarWavelet:

 Out[1]=

Use Normal to view all coefficients:

 Out[2]=

Transform an Image object:

 Out[1]=

Use to extract coefficient images:

 Out[2]=

Compute the inverse transform:

 Out[3]=

Transform a sampled Sound object:

 Out[1]=
 Out[2]=
 Out[3]=

Properties & Relations (12)Properties & Relations (12)Properties & Relations (12)Properties & Relations (12)

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