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http://dx.doi.org/10.7471/ikeee.2020.24.2.492

Nonlinear model for estimating depth map of haze removal  

Lee, Seungmin (Dept. of Electronics Engineering, Dong-A University)
Ngo, Dat (Dept. of Electronics Engineering, Dong-A University)
Kang, Bongsoon (Dept. of Electronics Engineering, Dong-A University)
Publication Information
Journal of IKEEE / v.24, no.2, 2020 , pp. 492-496 More about this Journal
Abstract
The visibility deteriorates in hazy weather and it is difficult to accurately recognize information captured by the camera. Research is being actively conducted to remove haze so that camera-based applications such as object localization/detection and lane recognition can operate normally even in hazy weather. In this paper, we propose a nonlinear model for depth map estimation through an extensive analysis that the difference between brightness and saturation in hazy image increases non-linearly with the depth of the image. The quantitative evaluation(MSE, SSIM, TMQI) shows that the proposed haze removal method based on the nonlinear model is superior to other state-of-the-art methods.
Keywords
Haze removal; Depth map; Nonlinear model; Machine learning; MLE;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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