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The Visual Computer

, Volume 32, Issue 5, pp 653–662 | Cite as

Adaptive transmission compensation via human visual system for efficient single image dehazing

  • Zhigang LingEmail author
  • Shutao Li
  • Yaonan Wang
  • He Shen
  • Xiao Lu
Original Article

Abstract

Dark channel prior has been used widely in single image haze removal because of its simple implementation and satisfactory performance. However, it often results in halo artifacts, noise amplification, over-darking, and/or over-saturation for some images containing heavy fog or large sky patches where dark channel prior is not established. To resolve this issue, this paper proposes an efficient single dehazing algorithm via adaptive transmission compensation based on human visual system (HVS). The key contributions of this paper are made as follows: firstly, two boundary constraints on transmission are deduced to preserve the intensity of the defogged image and suppress halo artifacts or noise via the minimum intensity constraint and the just-noticeable distortion model, respectively. Secondly, an improved HVS segmentation algorithm is employed to detect the saturation areas in the input image. Finally, an adaptive transmission compensation strategy is presented to remove the haze and simultaneously suppress the halo artifacts or noise in the saturation areas. Experimental results indicate that this proposed method can efficiently improve the visibility of the foggy images in the challenging condition.

Keywords

Single image dehazing Human visual system Just-noticeable distortion Dark channel prior 

Notes

Acknowledgments

This work was supported by the National High Technology Research and Development Program of China (863 Program, Grant No. 2012AA112312), National Natural Science Foundation of China (Grant No.61471166 and 61175075), the Science and Technique Project of Ministry of Transport of the People’s Republic of China (Grant No. 201231849A70) and Hunan Provincial Natural Science Foundation of China (14JJ2052).

References

  1. 1.
    Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Polarization-based vision through haze. Appl. Opt. 42(3), 511–525 (2003)CrossRefGoogle Scholar
  2. 2.
    Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. Int. J. Comput. Vis. 48(3), 233–254 (2002)CrossRefzbMATHGoogle Scholar
  3. 3.
    Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. 25(6), 713–724 (2003)CrossRefGoogle Scholar
  4. 4.
    Kopf, J., Neubert, B., Chen, B., Cohen, M.F., Deussen, O., Konstanz, et al.: Deep photo: model-based photograph enhancement and viewing. ACM Trans. Graph. 27(5), 116:1–116:10 (2008)CrossRefGoogle Scholar
  5. 5.
    Fattal, R.: Single Image Dehazing. ACM Trans. Graph. 27(3), 721–729 (2008)CrossRefGoogle Scholar
  6. 6.
    Tan, R.T.: Visibility in bad weather from a single image. In: IEEE International Conference on Computer Vision (CVPR). New York, USA (2008)Google Scholar
  7. 7.
    Xiao, C., Gan, J.: Fast image dehazing using guided joint bilateral filter. Vis. Comput. 28(6–8), 713–721 (2012)CrossRefGoogle Scholar
  8. 8.
    He, K., Sun, J., Tang, X: Single image haze removal using dark channel prior. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1956–1963 (2009)Google Scholar
  9. 9.
    Yan, W., Bo, W.: Improved single image dehazing using dark channel prior. In: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS), pp. 789–792 (2010)Google Scholar
  10. 10.
    Inhye, Y., Seonyung, K., Donggyun, K., Hayes, M.H., Joonki, P.: Adaptive defogging with color correction in the HSV color space for consumer surveillance system. IEEE Trans. Consum. Electron. 58(1), 111–116 (2012)CrossRefGoogle Scholar
  11. 11.
    Xie, B., Guo, F., Cai, Z.: Universal strategy for surveillance video defogging. Opt. Eng. 51(10), 1017031–1017037 (2012)CrossRefGoogle Scholar
  12. 12.
    Sun, W., Guo, B.L., Li, D.J., Jia, W.: Fast single-image dehazing method for visible-light systems. Opt. Eng. 52(9), 0931031–9 (2013)CrossRefGoogle Scholar
  13. 13.
    Zhang, J., Li, L., Zhang, Y., Yang, G., Cao, X., Sun, J.: Video dehazing with spatial and temporal coherence. Vis. Comput. 27, 749–757 (2011)CrossRefGoogle Scholar
  14. 14.
    Tripathi, A.K., Mukhopadhyay, S.: Single image fog removal using anisotropic diffusion. IET Image Proc. 6(7), 966–975 (2012)MathSciNetCrossRefGoogle Scholar
  15. 15.
    McCartney, E.J.: Optics of Atmosphere: Scattering by Molecules and Particles. Wiley, New York (1976)Google Scholar
  16. 16.
    Koschmieder, H.: Theorie der horizontaler Sichtweite Beitraege. Phys. Freib. Atmos. 12, 33–55 (1925)Google Scholar
  17. 17.
    Ancuti, C.O., Ancuti, C.: Single Image dehazing by multi-scale fusion. IEEE Trans. Image Proc. 22(8), 3271–3282 (2013)CrossRefGoogle Scholar
  18. 18.
    Li, W.J., Gu, B., Huang, J.T., Wang, S.Y., Wang, M.H.: Single image visibility enhancement in gradient domain. IET Image Proc. 6(5), 589–595 (2012)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Yan, Q., Xu, L., Jia, J.: Dense scattering layer removal. In: SIGGRAPH Asia 2013 Technical Briefs. ACM New York, NY, USAGoogle Scholar
  20. 20.
    Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: IEEE International Conference on Computer Vision (ICCV), pp. 617–624. Sydney, NSW (2013)Google Scholar
  21. 21.
    Chou, C., Li, Y.: A perceptually tuned subband image coder based on the measure of just-noticeable-distortion profile. IEEE Trans. Circ. Syst. Vid. 5(6), 467–476 (1995)CrossRefGoogle Scholar
  22. 22.
    Lee, C., Lin, P., Chen, L., Wang, W.: Image enhancement approach using the just-noticeable-difference model of the human visual system. J. Electron. Imag. 21(6), 33007 (2012)CrossRefGoogle Scholar
  23. 23.
    Panetta, K.A., Wharton, E.J., Agaian, S.S.: Human visual system-based image enhancement and logarithmic contrast measure. IEEE Trans. Syst., Man Cybern. B 38(1), 174–188 (2008)CrossRefGoogle Scholar
  24. 24.
    He, K., Sun, J., Tang, X.: Guided image filtering. In: The 11th European Conference on Computer Vision (ECCV), pp. 1–14. Heraklion, Crete, Greece (2010)Google Scholar
  25. 25.
    Tarel, J., Ere, N.H.: Fast visibility restoration from a single color or gray level image. In: IEEE International Conference on Computer Vision, pp. 2201–2208. New York, USA (2009)Google Scholar
  26. 26.
    Hautière, N., Tarel, J., Aubert, D., Dumont, É.: Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal. Stereol. 27(2), 87–95 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Proc. 13(4), 600–612 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Zhigang Ling
    • 1
    Email author
  • Shutao Li
    • 1
  • Yaonan Wang
    • 1
  • He Shen
    • 2
  • Xiao Lu
    • 1
  1. 1.College of Electrical and Information EngineeringHunan UniversityChangshaChina
  2. 2.Department of Mechanical and Aerospace EngineeringUniversity of Central FloridaOrlandoUSA

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