title: L2BGAN: An image enhancement model for image quality improvement and image analysis tasks without paired supervision
abstract: The paper presents an image enhancement model,
  L2BGAN, to translate low light images to bright images
  without a paired supervision. We introduce the use of geo-
  metric and lighting consistency along with a contextual loss
  criterion. These when combined with multiscale color, tex-
  ture and edge discriminators prove to provide competitive
  results. We perform extensive experiments on benchmark
  datasets to compare our results visually as well as objec-
  tively. We observe the performance of L2BGAN on real time
  driving datasets which are subject to motion blur, noise and
  other artifacts. We further demonstrate the application of
  image understanding tasks on our enhanced images using
  DarkFace and ExDark datasets.
  
authors: Jhilik Bhattacharya Gianni Ramponi Leonardo Gregorat Shatrughan Modi
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