Browse > Article
http://dx.doi.org/10.9717/kmms.2017.20.2.101

A LabVIEW-based Video Dehazing using Dark Channel Prior  

Roh, Chang Su (R&D Center, DAEDO TEKRA)
Kim, Yeon Gyo (R&D Center, DAEDO TEKRA)
Chong, Ui Pil (School of Electrical Engineering, University of Ulsa)
Publication Information
Journal of Korea Multimedia Society / v.20, no.2, 2017 , pp. 101-107 More about this Journal
Abstract
LabVIEW coding for video dehazing was developed. The dark channel prior proposed by K. He was applied to remove fog based on a single image, and K. B. Gibson's median dark channel prior was applied, and implemented in LabVIEW. In other words, we improved the image processing speed by converting the existing fog removal algorithm, dark channel prior, to the LabVIEW system. As a result, we have developed a real-time fog removal system that can be commercialized. Although the existing algorithm has been utilized, since the performance has been verified real - time, it will be highly applicable in academic and industrial fields. In addition, fog removal is performed not only in the entire image but also in the selected area of the partial region. As an application example, we have developed a system that acquires clear video from the long distance by connecting a laptop equipped with LabVIEW SW that was developed in this paper to a 100~300 times zoom telescope.
Keywords
Dehaze; Defog; Dark Channel Prior; LabVIEW; RGB; HSI;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 K. He, J. Sun, and X. Tang, "Single Image Haze Removal Using Dark Channel Prior," Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1956-1963, 2009.
2 K.B. Gibson, D.T. Vo, and T.Q. Nguyen. "An Inversigation of Dehazing Effects on Image and Video Coding," IEEE Transactions on Image Processing, Vol. 21, No. 2, pp. 662-673, 2012.   DOI
3 K.K. Gadnayak, P. Panda, and N. Panda, Haze Removal: An Approach Based on Saturation Component, L.C. Jain et al. (eds.), Advances in Intelligent Systems and Computing 309, Springer India, 2015.
4 J.W. Lee, and S.H. Hong, "Real-time Haze Removal Method using Brightness Transformation based on Atmospheric Scatter Coefficient Rate and Local Histogram Equalization," Journal of Korea Multimedia Society, Vol. 19, No. 1, pp. 10-21, 2016.   DOI
5 N. Kehtarnavaz and S. Mahotra, Digital Signal Processing Laboratory: LabVIEWBased FPGA Implementation, BrownWalker Press, Boca Raton, Florida USA, 2010.
6 K.S. Kwon and S. Ready, Practical Guide to Machine Vision Software: An Introduction with LabVIEW, Wiley-VCH Verlag GmbH & Co. KGaA, Boschstr. 12, 69469 Weinheim, Germany, 2015.