  We present a method to restore a clear image from a haze-affected image using
a Wasserstein generative adversarial network. As the problem is
ill-conditioned, previous methods have required a prior on natural images or
multiple images of the same scene. We train a generative adversarial network to
learn the probability distribution of clear images conditioned on the
haze-affected images using the Wasserstein loss function, using a gradient
penalty to enforce the Lipschitz constraint. The method is data-adaptive,
end-to-end, and requires no further processing or tuning of parameters. We also
incorporate the use of a texture-based loss metric and the L1 loss to improve
results, and show that our results are better than the current
state-of-the-art.
