Single Image Dehazing: An Analysis on Generative Adversarial Network

• 作者: Amina Khatun Mohammad Reduanul Haque Mohammad Shorif Uddin Rabeya Basri
• 来源期刊: 电脑和通信(英文)
• 学科: 工学
• 关键词: Dehazing DEEP Leaning Convulutional NEURAL NETWORK (CNN) GENERATIVE Adversarial Networks (GAN)
• 年，卷（期）: 2020,（4）
• 页码范围: 127-137
• 页数: 11页
• 分类号: TN3

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.