Li, R., Pan, J., Li, Z. and Tang, J. (2018) Single Image Dehazing via Conditional Generative Adversarial Network. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 8202-8211.
https://doi.org/10.1109/CVPR.2018.00856

has been cited by the following article:

• TITLE: Single Image Dehazing: An Analysis on Generative Adversarial Network

  AUTHORS: Amina Khatun, Mohammad Reduanul Haque, Rabeya Basri, Mohammad Shorif Uddin

  KEYWORDS: Dehazing, Deep Leaning, Convulutional Neural Network (CNN), Generative Adversarial Networks (GAN)

  JOURNAL NAME: Journal of Computer and Communications, Vol.8 No.4, April 30, 2020

  ABSTRACT: Haze is a very common phenomenon that degrades or reduces visibility. It causes various problems where high-quality images are required such as traffic and security monitoring. So haze removal from scenes is an immediate demand for clear vision. Recently, in addition to the conventional dehazing mechanisms, different types of deep generative adversarial networks (GAN) are applied to suppress the noise and improve the dehazing performance. But it is unclear how these algorithms would perform on hazy images acquired “in the wild” and how we could gauge the progress in the field. To bridge this gap, this presents a comprehensive study on three single image dehazing state-of-the-art GAN models, such as AOD-Net, cGAN, and DHSGAN. We have experimented using benchmark dataset consisting of both synthetic and real-world hazy images. The obtained results are evaluated both quantitatively and qualitatively. Among these techniques, the DHSGAN gives the best performance.