International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Single Image Dehazing: An Analysis on Generative Adversarial Network

  • Amina Khatun (Department of Computer Science and Engineering, Jahangirnagar University) ;
  • Mohammad Reduanul Haque (Department of Computer Science and Engineering, Daffodil International University) ;
  • Rabeya Basri (Department of Computer Science and Engineering, Jahangirnagar University) ;
  • Mohammad Shorif Uddin (Department of Computer Science and Engineering, Jahangirnagar University)
  • Received : 2024.02.05
  • Published : 2024.02.29
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Abstract

Haze is a very common phenomenon that degrades or reduces the visibility. It causes various problems where high quality images are required such as traffic and security monitoring. So haze removal from images receives great attention for clear vision. Due to its huge impact, significant advances have been achieved but the task yet remains a challenging one. Recently, 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. This paper aims to bridge this gap. We present a comprehensive study and experimental evaluation on diverse GAN models in single image dehazing through benchmark datasets.

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References

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