Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit

@article{Schawinski2017GenerativeAN,
  title={Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit},
  author={Kevin Schawinski and Ce Zhang and Hantian Zhang and Lucas Fowler and Gokula Krishnan Santhanam},
  journal={ArXiv},
  year={2017},
  volume={abs/1702.00403}
}
Observations of astrophysical objects such as galaxies are limited by various sources of random and systematic noise from the sky background, the optical system of the telescope and the detector used to record the data. Conventional deconvolution techniques are limited in their ability to recover features in imaging data by the Shannon-Nyquist sampling theorem. Here we train a generative adversarial network (GAN) on a sample of $4,550$ images of nearby galaxies at $0.01<z<0.02$ from the Sloan… Expand
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