EuroSciPy Astronomical Image Processing




Repository: GitHub

Description

This tutorial was prepared for the EuroScipy 2019 conference. The objective being to provide a brief introduction to some of the methods used to analyse astronomical images, in particular the use of sparsity to denoise images.

Please see the CosmoStat website for more signal and image processing tutorials.

Contents

Requirements

This tutorial can be run via the interactive online Binder interface. However, it is recommended to run it locally during the session to avoid wifi issues. Note that the following requirements need to be installed beforehand.

  1. python >=3.5
  2. astropy >=3.2.1
  3. jupyter >=1.0.0
  4. matplotlib >=3.1.1
  5. modopt >=1.4.0
  6. numpy >=1.16.4
  7. pysap (optional)
  8. sf_tools >=2.0.4

All of the requirements can be built with the provided conda enviroment

conda env create -f enviroment.yml

or using pip. The PySAP package can be installed with pip as follows:

pip install git+https://github.com/CEA-COSMIC/pysap

Note that PySAP requires cmake and a C++ compiler that supports OpenMP. See here for macOS help. All of the tutorial exercises can be run without PySAP if necessary.

Notebooks

  1. Introduction to Sparsity
  2. Astronomical Image Denoising