Astronomical Image Processing
09-03, 11:00–12:30 (UTC), Track4 (Chillida)

This tutorial will introduce the concept of sparsity and demonstrate how it can be used to remove noise from signals. These concepts will then be expanded to demonstrate how noise can be removed from astronomical images in particular.

Programme

  • The tutorial will begin with short introduction to the basic premise of sparsity and highlight some problems in astronomical image processing that can be solved using this methodology. (~15-20min; slides)
  • Tutees will then follow a hands-on demonstration of how the concept of sparsity can be used to denoise signals. (~30-35min; interactive jupyter notebook with exercises)
  • Finally the tutees will learn how to denoise an astronomical image and use their newfound skills to recover a nice picture of Saturn. (~35-40min; interactive jupyter notebook with an exercise)

Requirements

  • The tutorial contents are available on GitHub.
  • Provided tutees have a stable internet connection, the entire tutorial can be run online using Binder.
  • However, to be safe, tutees should download and install the tutorial materials beforehand.
Project Homepage / Git –

https://github.com/sfarrens/euroscipy

 Abstract as a tweet –

Astronomical Image Processing: denoising images of galaxies and planets with exercises in Jupyter.

 Python Skill Level –

basic

 Domain Expertise –

some

 Domains –

Astronomy, Image Processing
Samuel FARRENS
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