Journal of Computer and Communications

Volume 8, Issue 4 (April 2020)

ISSN Print: 2327-5219   ISSN Online: 2327-5227

Google-based Impact Factor: 1.24  Citations  

Single Image Dehazing: An Analysis on Generative Adversarial Network

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DOI: 10.4236/jcc.2020.84010    654 Downloads   1,328 Views  Citations

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.

Share and Cite:

Khatun, A. , Haque, M. , Basri, R. and Uddin, M. (2020) Single Image Dehazing: An Analysis on Generative Adversarial Network. Journal of Computer and Communications, 8, 127-137. doi: 10.4236/jcc.2020.84010.

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