Single Image Haze Removal Using Conditional Wasserstein Generative Adversarial Networks

被引:9
作者
Ebenezer, Joshua Peter [1 ]
Das, Bijaylaxmi [1 ]
Mukhopadhyay, Sudipta [1 ]
机构
[1] Indian Inst Technol Kharagpur, Dept Elect & Elect Commun Engn, Kharagpur, W Bengal, India
来源
2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | 2019年
关键词
VISIBILITY;
D O I
10.23919/eusipco.2019.8902992
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页数:5
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