Browse > Article
http://dx.doi.org/10.7471/ikeee.2021.25.1.83

Effective machine learning-based haze removal technique using haze-related features  

Lee, Ju-Hee (Dept. of Electronic Engineering, Dong-A University)
Kang, Bong-Soon (Dept. of Electronic Engineering, Dong-A University)
Publication Information
Journal of IKEEE / v.25, no.1, 2021 , pp. 83-87 More about this Journal
Abstract
In harsh environments such as fog or fine dust, the cameras' detection ability for object recognition may significantly decrease. In order to accurately obtain important information even in bad weather, fog removal algorithms are necessarily required. Research has been conducted in various ways, such as computer vision/data-based fog removal technology. In those techniques, estimating the amount of fog through the input image's depth information is an important procedure. In this paper, a linear model is presented under the assumption that the image dark channel dictionary, saturation ∗ value, and sharpness characteristics are linearly related to depth information. The proposed method of haze removal through a linear model shows the superiority of algorithm performance in quantitative numerical evaluation.
Keywords
machine learning; haze removal; haze-related features; maximum likelihood estimates; optimal depth map;
Citations & Related Records
연도 인용수 순위
  • Reference
1 K. He, J. Sun, and X. Tang, "Single Image Haze Removal Using Dark Channel Prior," IEEE Trans. Pattern Anal. Mach. Intell., vol.33, no.12, pp.2341-2353, 2011. DOI: 10.1109/TPAMI.2010.168   DOI
2 D. Ngo, G. D. Lee, and B. S. Kang, "Improved color attenuation prior for single-image haze removal," Appl. Sci., vol.9, no.19, pp.4011, 2019. DOI: 10.3390/app9194011   DOI
3 P. Xia and X. Liu, "Image dehazing technique based on polarimetric spectral analysis," Optik, vol.127, no.18, pp.7350-7358, 2016. DOI: 10.1016/j.ijleo.2016.05.071   DOI
4 Ngo, D.; Lee, S.; Kang, B. "Robust Single-Image Haze Removal Using Optimal Transmission Map and Adaptive Atmospheric Light," Remote Sens. Vol.12, pp.2233. 2020.   DOI
5 Ngo, D.; Lee, S.; Nguyen, Q.-H.; Ngo, T. M.; Lee, G.-D.; Kang, B. "Single Image Haze Removal from Image Enhancement Perspective for Real-Time Vision-Based Systems," Sensors, Vol.20, pp.5170, 2020.   DOI
6 J. P. Tarel and N. Hautiere, "Fast visivility restoration from a single color or gray level image," in Proc. of the 2009 IEEE International Conference on Computer Vision, pp.2201-2208, 2009. DOI: 10.1109/ICCV.2009.5459251   DOI
7 G. Kim, S. Lee, and B. Kang, "Single Image Haze Removal Using Hazy Particle Maps," IEICE Trans. Fundam. Electron. Commun. Comput.Sci., vol.101, no.11, pp.1999-2002, 2018. DOI: 10.1587/transfun.E101.A.1999   DOI
8 Q. Zhu, J. Mai, and L. Shao, "A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior," IEEE Trans. Image Process., vol.24, no.11, pp.3522-3533, 2015. DOI: 10.1109/TIP.2015.2446191   DOI
9 Y. Jiang, C. Sun, Y. Zhao and L. Yang, "Fog Density Estimation and Image Defogging Based on Surrogate Modeling for Optical Depth," in IEEE Transactions on Image Processing, vol.26, no.7, pp.3397-3409, 2017. DOI: 10.1109/TIP.2017.2700720.   DOI
10 D. Ngo, G. D. Lee, and B. S. Kang, "Improved color attenuation prior for single-image haze removal," Appl. Sci., vol.9, no.19, pp.4011, 2019. DOI: 10.3390/app9194011   DOI