Advertisement

Single Image Dehazing Based on Improved Dark Channel Prior and Unsharp Masking Algorithm

  • Liting Peng
  • Bo Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10954)

Abstract

In order to solve the problem of the “halo effect” and the bad color contrast after dehazing, a novel dehazing method based on the dark channel prior and the adaptive contrast enhancement algorithm is proposed. Using the hierarchical search method based on the quadratic tree space division to calculate the atmospheric light value, and then eliminate the “halo effect” caused by the guided filtering. By using the adaptive contrast enhancement algorithm based on unsharp masking algorithm to improve image information at the haze high concentration regional. Experimental results show that this algorithm can be more effective to dehaze and images after dehazing have a higher contrast.

Keywords

Dark channel prior Guided filter Single image Unsharp masking 

References

  1. 1.
    Choi, L.K., You, J., Bovik, A.C.: Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans. Image Process. 24(11), 3888–3901 (2015)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Tan, R.: Visibility in bad weather from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  3. 3.
    Fattal, R.: Single image dehazing. In: Proceedings of the ACM SIGGRAPH, pp. 1–9 (2008)Google Scholar
  4. 4.
    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2009)Google Scholar
  5. 5.
    He, K., Sun, J., Tang, X.: Guided image filtering. In: Proceedings of the European Conference on Computer Vision, vol. 6311, pp. 1–14 (2010)Google Scholar
  6. 6.
    Yang, Y.J., Fu, Z.Z., Li, X.Y., et al.: Improved single image dehazing using dark channel prior. J. Syst. Eng. Electron. 26(5), 1070–1079 (2015)CrossRefGoogle Scholar
  7. 7.
    Kim, J.H., Jang, W.D., Sim, J.Y., Kim, C.S.: Optimized contrast enhancement for real-time image and video dehazing. J. Vis. Commun. Image Represent. 24(3), 410–425 (2013)CrossRefGoogle Scholar
  8. 8.
    Peng, Y.-T., Cao, K.: Pamela: generalization of the dark channel prior for single image restoration. IEEE Trans. Image Process. 6(27), 2856–2868 (2018)CrossRefGoogle Scholar
  9. 9.
    Koschmieder, E.L.: Benard convection. Adv. Chem. Phys. 26(177–212), 605 (1974)Google Scholar
  10. 10.
    Narasimhan, S.G., Nayar, S.K.: Chromatic framework for vision in bad weather. In: CVPR, pp. 598–605 (2000)Google Scholar
  11. 11.
    Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. IJCV 48, 233–254 (2002)Google Scholar
  12. 12.
    Zomet, A., Peleg, S.: Multi-sensor super resolution. In: Proceedings of IEEE Workshop Applications of Computer Vision (2002)Google Scholar
  13. 13.
    Levin, A., Lischinski, D., Weiss, Y.: A closed form solution to natural image matting. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2006)Google Scholar
  14. 14.
    Polesel, A., Mathews, V.J., Ramponi, G.: Image enhancement via adaptive unsharp masking. IEEE Trans. Image Process. 9(3), 505–510 (2000)CrossRefGoogle Scholar
  15. 15.
    Draper, N., Smith, H.: Applied Regression Analysis, 2nd edn. Wiley, New York (1981)zbMATHGoogle Scholar
  16. 16.
    Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning. Springer, New York (2003).  https://doi.org/10.1007/978-0-387-84858-7CrossRefzbMATHGoogle Scholar
  17. 17.
    Wang, K., Dunn, E., Tighe, J.: Combining semantic scene priors and haze removal for single image depth estimation. In: IEEE WACV (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer Science and Technology, Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial SystemWuhan University of Science and TechnologyWuhanChina

Personalised recommendations