Single Image Haze Removal Using Conditional Wasserstein Generative Adversarial Networks
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.
Code (1)
Tasks
Generative Adversarial NetworkImage DehazingSingle Image Haze RemovalSimilar Papers 제목 키워드 기반
CANDY: Conditional Adversarial Networks based Fully End-to-End System for Single Image Haze Removal
Single image haze removal is a very challenging and ill-posed problem. The existing haze removal methods in literature, including the recently introduced deep learning methods, model the problem of haze removal as that o…
Single Image Haze RemovalA Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior
Single image haze removal has been a challenging problem due to its ill-posed nature. In this paper, we propose a simple but powerful color attenuation prior for haze removal from a single input hazy image. By creating a…
Single Image DehazingSingle Image Haze RemovalSingle Image Haze Removal Using Dark Channel Prior
In this paper, we propose a simple but effective image prior-dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of outdoor haze-free images. It is based on a key o…
Single Image Haze RemovalDehazeNet: An End-to-End System for Single Image Haze Removal
Single image haze removal is a challenging ill-posed problem. Existing methods use various constraints/priors to get plausible dehazing solutions. The key to achieve haze removal is to estimate a medium transmission map …
Image DehazingSingle Image Haze RemovalSingle Image Haze Removal using a Generative Adversarial Network
Traditional methods to remove haze from images rely on estimating a transmission map. When dealing with single images, this becomes an ill-posed problem due to the lack of depth information. In this paper, we propose an …
Generative Adversarial NetworkImage DehazingSingle Image Haze Removal