# Single Image Haze Removal Using Conditional Wasserstein Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1903.00395 · HTML: https://mlsanity.com/paper/1903.00395/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [About exchanging expectation and supremum for conditional Wasserstein GANs](https://mlsanity.com/paper/2103.13906/) (2021)
- [Unified Framework for Histopathology Image Augmentation and Classification via Generative Models](https://mlsanity.com/paper/2212.09977/) (2022)
- [Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model](https://mlsanity.com/paper/2106.12226/) (2021)

## Abstract (arXiv)

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.