pyShearLab - A Python Shearlet Toolbox
pyShearLab is a Python toolbox which is based on ShearLab3D
written by Rafael Reisenhofer. Currently, pyShearLab only offers a two-dimensional subset
of ShearLab3D which contains both 2D and 3D transforms.
The toolbox needs the following Python packages in order to work properly:
pyShearLab2D has been developed and tested with Python 3.6 using the Anaconda package
on Linux (Ubuntu 16.04.2 LTS), Windows 10 and Mac OS X (10.11-10.12).
You can simply download, unzip and use pyShearLab. Depending on your specific Python
development environment, you may want to add the pyShearLab2D folder to your Python
environment (Python Path). The dependencies can be installed using pip. If you use
Anaconda, they are already installed.
In order to use pyShearLab you need to import it as a module, see pySLExampleDenoising.py
as an example. The denoising example provides all neccessary steps to understand how to
use the toolbox. When using the transform in an iterative scheme, the creation of shearlet
system can be done in a pre-processing step which significantly speeds up the process.
Please note that the images have to be square.
pyShearLab was written by Stefan Loock who acknowledges funding by the
SFB 755 Nanoscale Photonic Imaging.
pyShearLab is based on ShearLab3D
which is written by Rafael Reisenhofer and published under the GPL.
The toolbox uses some functions from
which have been translated to Python.