{
  "id": "1903.00395",
  "version": "v1",
  "published": "2019-03-01T16:32:05.000Z",
  "updated": "2019-03-01T16:32:05.000Z",
  "title": "Single Image Haze Removal Using Conditional Wasserstein Generative Adversarial Networks",
  "authors": [
    "Joshua Peter Ebenezer",
    "Bijaylaxmi Das",
    "Sudipta Mukhopadhyay"
  ],
  "comment": "5 pages",
  "categories": [
    "cs.CV"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-03-01T16:32:05.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "conditional wasserstein generative adversarial networks",
      "single image haze removal",
      "clear image"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 5,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}