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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 9739201, 8 pages
https://doi.org/10.1155/2017/9739201
Research Article

Incident Light Frequency-Based Image Defogging Algorithm

School of Energy Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Correspondence should be addressed to Wenbo Zhang; moc.361@toorts and Xiaorong Hou; nc.ude.ctseu@rxuoh

Received 12 December 2016; Revised 29 March 2017; Accepted 2 April 2017; Published 10 April 2017

Academic Editor: Jean Jacques Loiseau

Copyright © 2017 Wenbo Zhang and Xiaorong Hou. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. H. Zhang, X. Liu, and Y. Cheung, “Efficient single image dehazing via scene-adaptive segmentation and improved dark channel model,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '16), Vancouver, Canada, July 2016. View at Publisher · View at Google Scholar
  2. M. Ju, D. Zhang, and X. Wang, “Single image dehazing via an improved atmospheric scattering model,” The Visual Computer, pp. 1–13, 2016. View at Publisher · View at Google Scholar
  3. H. Lu, Y. Li, S. Nakashima, and S. Serikawa, “Single image dehazing through improved atmospheric light estimation,” Multimedia Tools and Applications, vol. 75, no. 24, pp. 17081–17096, 2016. View at Publisher · View at Google Scholar · View at Scopus
  4. Z. Mi, H. Zhou, Y. Zheng, and M. Wang, “Single image dehazing via multi-scale gradient domain contrast enhancement,” IET Image Processing, vol. 10, no. 3, pp. 206–214, 2016. View at Publisher · View at Google Scholar · View at Scopus
  5. T. Cui, L. Qu, J. Tian, and Y. Tang, “Single image haze removal based on luminance weight prior,” in Proceedings of the IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER '16), pp. 332–336, Chengdu, China, June 2016. View at Publisher · View at Google Scholar
  6. Z. Tang, X. Zhang, X. Li, and S. Zhang, “Robust image hashing with ring partition and invariant vector distance,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 1, pp. 200–214, 2016. View at Publisher · View at Google Scholar · View at Scopus
  7. Z. Gao and Y. Bai, “Single image haze removal algorithm using pixel-based airlight constraints,” in Proceedings of the 22nd International Conference on Automation and Computing (ICAC '16): Tackling the New Challenges in Automation and Computing, no. 1, pp. 267–272, Colchester, UK, 2016.
  8. S. Santra and B. Chanda, “Single image dehazing with varying atmospheric light intensity,” in Proceedings of the 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG '15), December 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. Q. Zhu, J. 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, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. B. Huo, F. Yin, and B. Polytechnic, “Image dehazing with dark channel prior and novel estimation,” International Journal of Multimedia and Ubiquitous Engineering, vol. 10, no. 3, pp. 13–22, 2015. View at Google Scholar
  11. C. Science and M. Studies, “Image enhancement techniques for different atmospheric conditions,” International Journal of Advance Research in Computer Science and Management Studies, vol. 3, no. 2, pp. 49–52, 2015. View at Google Scholar
  12. L. K. Choi, J. You, and A. C. Bovik, “Referenceless prediction of perceptual fog density and perceptual image defogging,” IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3888–3901, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. H. Zhao, C. Xiao, J. Yu, and X. Xu, “Single image fog removal based on local extrema,” IEEE/CAA Journal of Automatica Sinica, vol. 2, no. 2, pp. 158–165, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  14. L. Zhang, X. Li, B. Hu, and X. Ren, “Research on fast smog free algorithm on single image,” in Proceedings of the 1st International Conference on Computational Intelligence Theory, Systems and Applications (CCITSA '15), pp. 177–182, IEEE, Yilan, Taiwan, December 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. Y.-K. Wang and C.-T. Fan, “Single image defogging by multiscale depth fusion,” IEEE Transactions on Image Processing, vol. 23, no. 11, pp. 4826–4837, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  16. Z. Tang, X. Zhang, and S. Zhang, “Robust perceptual image hashing based on ring partition and NMF,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 3, pp. 711–724, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient image dehazing with boundary constraint and contextual regularization,” in Proceedings of the 14th IEEE International Conference on Computer Vision (ICCV '13), pp. 617–624, December 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. J.-P. Tarel and N. Hautiere, “Fast visibility restoration from a single color or gray level image,” in Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV '09), pp. 2201–2208, IEEE, Kyoto, Japan, September 2009. View at Publisher · View at Google Scholar
  19. K. Zuiderveld, “Contrast limited adaptive histogram equalization,” in Graphics Gems IV, P. S. Heckbert, Ed., pp. 474–485, Academic Press Professional, San Diego, Calif, USA, 1994. View at Google Scholar
  20. Y. Xu, J. Wen, L. Fei, and Z. Zhang, “Review of video and image defogging algorithms and related studies on image restoration and enhancement,” IEEE Access, vol. 4, pp. 165–188, 2016. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Lee, S. Yun, J.-H. Nam, C. S. Won, and S.-W. Jung, “A review on dark channel prior based image dehazing algorithms,” EURASIP Journal on Image and Video Processing, vol. 2016, article 4, 2016. View at Publisher · View at Google Scholar · View at Scopus
  22. Z. Pei, Z. Hong, Q. I. Xue-ming, and L. Han, “An image clearness method for fog,” Journal of Image and Graphics, vol. 9, no. 1, pp. 124–128, 2004. View at Google Scholar
  23. Y. Yitzhaky, I. Dror, and N. S. Kopeika, “Restoration of atmospherically blurred images according to weather-predicted atmospheric modulation transfer functions,” Optical Engineering, vol. 36, no. 11, pp. 3064–3072, 1997. View at Publisher · View at Google Scholar · View at Scopus
  24. K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2341–2353, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. 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, 2003. View at Publisher · View at Google Scholar · View at Scopus
  26. S. G. Narasimhan and S. K. Nayar, “Vision and the atmosphere,” International Journal of Computer Vision, vol. 48, no. 3, pp. 233–254, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  27. Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar, “Instant dehazing of images using polarization,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), vol. 1, pp. I325–I332, December 2001. View at Scopus
  28. F. Cozman and E. Krotkov, “Depth from scattering,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 801–806, June 1997. View at Scopus
  29. S. K. Nayar and S. G. Narasimhan, “Vision in bad weather,” in Proceedings of the 17th IEEE International Conference on Computer Vision ( ICCV '99), vol. 2, pp. 820–827, Kerkyra, Greece, 1999.
  30. 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, 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. D. F. Swinehart, “The Beer-Lambert law,” Journal of Chemical Education, vol. 39, no. 7, pp. 333–335, 1962. View at Publisher · View at Google Scholar · View at Scopus
  32. Y. Xu, J. Wen, L. Fei, and Z. Zhang, “Implementation code of clahe,” http://www.yongxu.org/code/the%20survey%20of%20defogging.zip.
  33. J.-P. Tarel and N. Hautiere, “Implementation code of fast visibility restoration from a single color or gray level image,” http://perso.lcpc.fr/tarel.jean-philippe/visibility/visibresto2.zip.
  34. G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Implementation code of efficient image dehazing with boundary constraint and contextual regularization,” http://www.escience.cn/people/menggaofeng/research.html.