文章基本信息

标题：Single Image Dehazing: An Analysis on Generative Adversarial Network
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作者：Amina Khatun ; Mohammad Reduanul Haque ; Rabeya Basri 等
期刊名称：Journal of Computer and Communications
印刷版ISSN：2327-5219
电子版ISSN：2327-5227
出版年度：2020
卷号：8
期号：4
页码：127-137
DOI：10.4236/jcc.2020.84010
出版社：Scientific Research Publishing
摘要：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.
关键词：Dehazing;Deep Leaning;Convulutional Neural Network (CNN);Generative Adversarial Networks (GAN)