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http://dx.doi.org/10.9717/kmms.2021.24.10.1319

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

Sim, Hwi Bo (Dept. of Electronics Engineering, Graduate School, Dong-A University)
Kang, Bong Soon (Dept. of Electronics Engineering Dong-A University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.10, 2021 , pp. 1319-1325 More about this Journal
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.
Keywords
De-fogging; Deep learning; Domain shift; U-net; Loss function;
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