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Real-time Defog Processing Using Cooperative Networks

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(10), pp.89-96
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : August 1, 2024
  • Accepted : September 25, 2024
  • Published : October 31, 2024

Sanghyun Jung 1

1주식회사 에너자이

Accredited

ABSTRACT

In this paper, we propose a deep learning model and inference pipeline that can process high-resolution fog video in real-time, addressing limitations found in classical defogging algorithms and existing deep learning-based defogging models. The key idea is separating the tasks of inferring fog color and estimating the amount of fog into two distinct models, allowing for a more efficient, lightweight design that improves inference speed. While many deep defogging models perform well on synthetic fog images, they suffer from reduced effectiveness on real-world fog images with diverse fog colors and backgrounds. We solve this problem by introducing a synthetic fog dataset generation method tailored for real-world conditions. Through experiments, we demonstrate the increase in visible distance achieved by proposed model and compare its inference speed and defogging performance against pre-trained models on real-world CCTV fog images.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.