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Dual-Tree Complex Wavelet Transform library for Python

This library provides support for computing 1D, 2D and 3D dual-tree complex wavelet transforms and their inverse in Python. Full documentation is available online. Coverage License Latest Version Downloads DOI: 10.5281/zenodo.9862 Documentation Status


Ubuntu 15.10 (wily) and later

Installation can be perfomed via apt-get:

$ sudo apt-get install python-dtcwt python-dtcwt-doc

The package is also currently in Debian sid (unstable).

Other operating systems

The easiest way to install dtcwt is via easy_install or pip:

$ pip install dtcwt

If you want to check out the latest in-development version, look at the project's GitHub page. Once checked out, installation is based on setuptools and follows the usual conventions for a Python project:

$ python install

(Although the develop command may be more useful if you intend to perform any significant modification to the library.) A test suite is provided so that you may verify the code works on your system:

$ pip install -r tests/requirements.txt
$ py.test

This will also write test-coverage information to the cover/ directory.

Further documentation

There is more documentation available online and you can build your own copy via the Sphinx documentation system:

$ python build_sphinx

Compiled documentation may be found in build/docs/html/.


Based on the Dual-Tree Complex Wavelet Transform Pack for MATLAB by Nick Kingsbury, Cambridge University. The original README can be found in ORIGINAL_README.txt. This file outlines the conditions of use of the original MATLAB toolbox.




  • Use fixed random number generator seed when generating documentation.
  • Replace use of Lena image with mandrill.
  • Refactor test suite to use tox + py.test.
  • Documentation formatting fixes.
  • Fix unsafe use of inplace casting (3D transform).
  • Use explicit integer division to close #123.


  • Fix regression in dtcwt-based image registration.
  • Allow levels used for dtcwt-based image registration to be customised.


  • Add queue parameter to low-level OpenCL colifilt and coldfilt functions.
  • Significantly increase speed of dtcwt.registration.estimatereg function.
  • Fix bug whereby dtcwt.backend_name was not restored when using preserve_backend_stack.


  • The OpenCL 2D transform was not always using the correct queue when one was passed explicitly.


  • MATLAB-style functions such as dtwavexfm2 have been moved into a separate dtcwt.compat module.
  • Backends moved to dtcwt.numpy and dtcwt.opencl modules.
  • Removed dtcwt.base.ReconstructedSignal which was a needless wrapper around NumPy arrays.
  • Rename TransformDomainSignal to Pyramid.
  • Allow runtime configuration of default backend via dtcwt.push_backend function.
  • Verified, thanks to @timseries, the NumPy 3D transform implementation against the MATLAB reference implementation.


  • Verified the highpass re-sampling routines in dtcwt.sampling against the existing MATLAB implementation.
  • Added experimental image registration routines.
  • Re-organised documentation.


  • Fixed regression from 0.7 where backend_opencl.dtwavexfm2 would return None, None, None.


  • Fix a memory leak in OpenCL implementation where transform results were never de-allocated.


Python port of the Dual-Tree Complex Wavelet Transform toolbox for MATLAB




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