Single Image Haze Removal Using Conditional Wasserstein Generative Adversarial Networks
This addresses the challenge of single-image haze removal for computer vision applications, offering an incremental improvement over existing methods.
The paper tackles the problem of restoring clear images from single haze-affected images by training a conditional Wasserstein generative adversarial network, achieving results that outperform the current state-of-the-art.
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.