pywi 0.1.dev8

Python Wavelet Imaging

Copyright (c) 2016-2018 Jeremie DECOCK (www.jdhp.org)

• Online documentation: https://jeremiedecock.github.io/pywi/

• Source code: https://github.com/jeremiedecock/pywi

• Issue tracker: https://github.com/jeremiedecock/pywi/issues

• PyWI on PyPI: https://pypi.org/project/pywi/

Description

PyWI is an image filtering library aimed at removing additive background noise from raster graphics images.

• Input: a FITS file containing the raster graphics to clean (i.e. an image defined as a classic rectangular lattice of square pixels).

• Output: a FITS file containing the cleaned raster graphics.

The image filter relies on multiresolution analysis methods (Wavelet transforms) that remove some scales (frequencies) locally in space. These methods are particularly efficient when signal and noise are located at different scales (or frequencies). Optional features improve the SNR ratio when the (clean) signal constitute a single cluster of pixels on the image (e.g. electromagnetic showers produced with Imaging Atmospheric Cherenkov Telescopes). This library is written in Python and is based on the existing Cosmostat tools iSAp (Interactive Sparse Astronomical data analysis Packages http://www.cosmostat.org/software/isap/).

The PyWI library also contains a dedicated package to optimize the image filter parameters for a given set of images (i.e. to adapt the filter to a specific problem). From a given training set of images (containing pairs of noised and clean images) and a given performance estimator (a function that assess the image filter parameters comparing the cleaned image to the actual clean image), the optimizer can determine the optimal filtering level for each scale.

The PyWI library contains:

• wavelet transform and wavelet filtering functions for image multiresolution analysis and filtering;

• additional filter to remove some image components (non-significant pixels clusters);

• a set of generic filtering performance estimators (MSE, NRMSE, SSIM, PSNR, image moment’s difference), some relying on the scikit-image Python library (supplementary estimators can be easily added to meet particular needs);

• a graphical user interface to visualize the filtering process in the wavelet transformed space;

• an Evolution Strategies (ES) algorithm known in the mathematical optimization community for its good convergence rate on generic derivative-free continuous global optimization problems (Beyer, H. G. (2013) “The theory of evolution strategies”, Springer Science & Business Media);

• additional tools to manage and monitor the parameter optimization.

Note:

This project is in beta stage.

Dependencies

• Python >= 3.0

• Numpy

• Scipy

• Scikit-image

• _Cosmostat _iSAP Sparce2D

Installation

Gnu/Linux

You can install, upgrade, uninstall PyWI with these commands (in a terminal):

pip install --pre pywi
pip install --upgrade pywi
pip uninstall pywi

Or, if you have downloaded the PyWI source code:

python3 setup.py install

Windows

You can install, upgrade, uninstall PyWI with these commands (in a command prompt):

py -m pip install --pre pywi
py -m pip install --upgrade pywi
py -m pip uninstall pywi

Or, if you have downloaded the PyWI source code:

py setup.py install

MacOSX

You can install, upgrade, uninstall PyWI with these commands (in a terminal):

pip install --pre pywi
pip install --upgrade pywi
pip uninstall pywi

Or, if you have downloaded the PyWI source code:

python3 setup.py install

Cosmostat iSAP Sparce2D installation

1. Download http://www.cosmostat.org/wp-content/uploads/2014/12/ISAP_V3.1.tgz (see http://www.cosmostat.org/software/isap/)

2. Unzip this archive, go to the “sparse2d” directory and compile the sparse2d library. It should generate two executables named mr_transform and mr_filter:

   tar -xzvf ISAP_V3.1.tgz
   cd ISAP_V3.1/cxx
   tar -xzvf sparse2d_V1.1.tgz
   cd sparse2d
   compile the content of this directory

Example

1. Download a sample image (shower.fits)

2. In your system terminal, type:

   pywi_mrfilter shower.fits

3. Use the -h option for more options

A “benchmark mode” can also be used to clean images and assess cleaning algorithms (it’s still a bit experimental): use the additional option -b all in each command (and put several fits files in input e.g. \*.fits)

Bug reports

To search for bugs or report them, please use the PyWI Bug Tracker at:

https://github.com/jeremiedecock/pywi/issues