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I'm interested in using a SWT to perform Multi Resolution Analysis over 3D data arrays. However I could not find any software package that implements it. The Matlab Toolbox of wavelets (the most complete I think) only has 3D DWT and 2D SWT, so my questions are:

  1. There exists a working implementation of 3D SWT out there?
  2. If not, how can I implement it? (some references maybe)
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  • $\begingroup$ Have you checked the Wikipedia article on the topic? It has 8 references $\endgroup$ – nicoguaro♦ Jun 12 '16 at 17:05
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    $\begingroup$ L2W implements a locally stationary wavelet approach, is that useful to you? Link: github.com/cran/LS2W $\endgroup$ – nicoguaro♦ Jun 12 '16 at 17:07
  • $\begingroup$ I know this is YOUR question. But do you really need 3D SWT? It could be huge, and less efficient than other less redundant methods? $\endgroup$ – Laurent Duval Jun 12 '16 at 20:54
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There as a general n-dimensional SWT for Python in the PyWavelets package as of the 0.5.0 release.

Basic usage with data stored in a NumPy array would be as follows (shown here for a 4-level decomposition and Debauchies 'db2' wavelet). PyWavelets uses the same wavelet naming conventions as the Matlab Wavelet Toolbox.

import pywt
coeffs = pywt.swtn(data, wavelet='db2', level=4)

The example above takes <2 seconds for a 128x128x128 data array on my system.

If you are willing to build from source, there is also an n-dimensional inverse SWT available in the master branch. Note that the inverse stationary wavelet transform is not currently implemented in a very efficient manner (particular for larger number of levels of decomposition).

In 3D, the SWT is redundant by a factor of (1 + 7*L) for an L-level decomposition (Although the implementation in PyWavelets currently returns the approximation coefficients at every level, not just the final one, leading to a redundancy of 8*L in 3D).

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  • $\begingroup$ Great news!! I hope PyWavelet package keep improving over time. $\endgroup$ – mavillan Jan 12 '17 at 17:49

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