Deep learning for a space-variant deconvolution in galaxy surveys

  title={Deep learning for a space-variant deconvolution in galaxy surveys},
  author={Florent Sureau and Alexis Lechat and Jean-Luc Starck},
  journal={Astronomy \& Astrophysics},
The deconvolution of large survey images with millions of galaxies requires developing a new generation of methods that can take a space-variant point spread function into account. These methods have also to be accurate and fast. We investigate how deep learning might be used to perform this task. We employed a U-net deep neural network architecture to learn parameters that were adapted for galaxy image processing in a supervised setting and studied two deconvolution strategies. The first… 

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