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Deep learning-based de-fogging method using fog features to solve the domain shift problem

Domain Shift 문제를 해결하기 위해 안개 특징을 이용한 딥러닝 기반 안개 제거 방법

  • Sim, Hwi Bo (Dept. of Electronics Engineering, Graduate School, Dong-A University) ;
  • Kang, Bong Soon (Dept. of Electronics Engineering Dong-A University)
  • 심휘보 ;
  • 강봉순
  • Received : 2021.08.10
  • Accepted : 2021.09.27
  • Published : 2021.10.30

Abstract

It is important to remove fog for accurate object recognition and detection during preprocessing because images taken in foggy adverse weather suffer from poor quality of images due to scattering and absorption of light, resulting in poor performance of various vision-based applications. This paper proposes an end-to-end deep learning-based single image de-fogging method using U-Net architecture. The loss function used in the algorithm is a loss function based on Mahalanobis distance with fog features, which solves the problem of domain shifts, and demonstrates superior performance by comparing qualitative and quantitative numerical evaluations with conventional methods. We also design it to generate fog through the VGG19 loss function and use it as the next training dataset.

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