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Single Image Dehazing Using Dark Channel Prior and Minimal Atmospheric Veil

  • Zhou, Xiao (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Wang, Chengyou (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Wang, Liping (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Wang, Nan (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Fu, Qiming (School of Mechanical, Electrical and Information Engineering, Shandong University)
  • Received : 2015.07.05
  • Accepted : 2015.10.27
  • Published : 2016.01.31

Abstract

Haze or fog is a common natural phenomenon. In foggy weather, the captured pictures are difficult to be applied to computer vision system, such as road traffic detection, target tracking, etc. Therefore, the image dehazing technique has become a hotspot in the field of image processing. This paper presents an overview of the existing achievements on the image dehazing technique. The intent of this paper is not to review all the relevant works that have appeared in the literature, but rather to focus on two main works, that is, image dehazing scheme based on atmospheric veil and image dehazing scheme based on dark channel prior. After the overview and a comparative study, we propose an improved image dehazing method, which is based on two image dehazing schemes mentioned above. Our image dehazing method can obtain the fog-free images by proposing a more desirable atmospheric veil and estimating atmospheric light more accurately. In addition, we adjust the transmission of the sky regions and conduct tone mapping for the obtained images. Compared with other state of the art algorithms, experiment results show that images recovered by our algorithm are clearer and more natural, especially at distant scene and places where scene depth jumps abruptly.

Keywords

References

  1. P. Babu and V. Rajamani, “Contrast enhancement using real coded genetic algorithm based modified histogram equalization for gray scale images,” International Journal of Imaging Systems and Technology, vol. 25, no. 1, pp. 24-32, Mar. 2015. Article (CrossRef Link). https://doi.org/10.1002/ima.22117
  2. B. Xie, F. Guo, and Z. X. Cai, "Improved single image dehazing using dark channel prior and multi-scale Retinex," in Proc. of the International Conference on Intelligent System Design and Engineering Application, Changsha, China, Oct. 13-14, vol. 1, pp. 848-851, 2010. Article (CrossRef Link).
  3. S. G. Narasimhan and S. K. Nayar, “Contrast restoration of weather degraded images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 6, pp. 713-724, Jun. 2003. Article (CrossRef Link). https://doi.org/10.1109/TPAMI.2003.1201821
  4. D. Miyazaki, D. Akiyama, M. Baba, R. Furukawa, S. Hiura, and N. Asada, "Polarization-based dehazing using two reference objects," in Proc. of the IEEE International Conference on Computer Vision Workshops, Sydney, Australia, Dec. 2-8, pp. 852-859, 2013. Article (CrossRef Link).
  5. S. G. Narasimhan and S. K. Nayar, "Contrast restoration of weather degraded images," in Proc. of the ACM SIGGRAPH Asia Courses, Singapore, Singapore, Dec. 10-13, 12 pages, Article number: 70, 2008. Article (CrossRef Link).
  6. J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, “Deep photo: Model-based photograph enhancement and viewing,” ACM Transactions on Graphics, vol. 27, no. 5, 10 pages, Article number: 116, Dec. 2008. Article (CrossRef Link). https://doi.org/10.1145/1409060.1409069
  7. R. T. Tan, "Visibility in bad weather from a single image," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, USA, Jun. 23-28, 8 pages, Article number: 4587643, 2008. Article (CrossRef Link).
  8. R. Fattal, “Single image dehazing,” ACM Transactions on Graphics, vol. 27, no. 3, 9 pages, Article number: 72, Aug. 2008. Article (CrossRef Link). https://doi.org/10.1145/1360612.1360671
  9. K. M. He, J. Sun, and X. O. Tang, "Single image haze removal using dark channel prior," in Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Miami, USA, Jun. 20-25, pp. 1956-1963, 2009. Article (CrossRef Link).
  10. A. Levin, D. Lischinski, and Y. Weiss, “A closed-form solution to natural image matting,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp. 228-242, Feb. 2008. Article (CrossRef Link). https://doi.org/10.1109/TPAMI.2007.1177
  11. K. M. He, J. Sun, and X. O. Tang, “Guided image filtering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1397-1409, Jun. 2013. Article (CrossRef Link). https://doi.org/10.1109/TPAMI.2012.213
  12. J. P. Tarel and N. Hautiere, "Fast visibility restoration from a single color or gray level image," in Proc. of the IEEE 12th International Conference on Computer Vision, Kyoto, Japan, Sep. 29-Oct. 2, pp. 2201-2208, 2009. Article (CrossRef Link).
  13. G. Y. Wang, G. H. Ren, L. H. Jiang, and T. F. Quan, “Single image dehazing algorithm based on sky region segmentation,” Information Technology Journal, vol. 12, no. 6, pp. 1168-1175, Jun. 2013. Article (CrossRef Link). https://doi.org/10.3923/itj.2013.1168.1175
  14. Z. G. Li, J. H. Zheng, Z. J. Zhu, W. Yao, and S. Q. Wu, “Weighted guided image filtering,” IEEE Transactions on Image Processing, vol. 24, no. 1, pp. 120-129, Jan. 2015. Article (CrossRef Link). https://doi.org/10.1109/TIP.2014.2371234
  15. Z. G. Li and J. H. Zheng, “Edge-preserving decomposition-based single image haze removal,” IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5432-5441, Dec. 2015. Article (CrossRef Link). https://doi.org/10.1109/TIP.2015.2482903
  16. Q. S. Zhu, J. M. Mai, and L. Shao, “A fast single image haze removal algorithm using color attenuation prior,” IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3522-3533, Nov. 2015. Article (CrossRef Link). https://doi.org/10.1109/TIP.2015.2446191
  17. K. Sun, B. Wang, Z. Q. Zhou, and Z. H. Zheng, “Real time image haze removal using bilateral filter,” Transactions of Beijing Institute of Technology, vol. 31, no. 7, pp. 810-813, 822, Jul. 2011. (in Chinese) Article (CrossRef Link).
  18. C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images," in Proc. of the IEEE Conference on Computer Vision, Bombay, India, Jan. 4-7, pp. 839-846, 1998. Article (CrossRef Link).
  19. C. X. Xiao and J. J. Gan, “Fast image dehazing using guided joint bilateral filter,” Visual Computer, vol. 28, no. 6-8, pp. 713-721, Jun. 2012. Article (CrossRef Link). https://doi.org/10.1007/s00371-012-0679-y
  20. J. T. Lou, W. Wang, Y. L. Li, and M. J. Zhang, “A fast approach to remove the haze from a single Bayer image,” Journal of National University of Defense Technology, vol. 35, no. 6, pp. 109-115, Dec. 2013. Article (CrossRef Link).
  21. F. Drago, K. Myszkowski, T. Annen, and N. Chiba, “Adaptive logarithmic mapping for displaying high contrast scenes,” Computer Graphics Forum, vol. 22, no. 3, pp. 419-426, Sep. 2003. Article (CrossRef Link). https://doi.org/10.1111/1467-8659.00689

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