Journal of Real-Time Image Processing

, Volume 16, Issue 6, pp 1959–1973 | Cite as

Real-time haze removal in monocular images using locally adaptive processing

  • Victor H. Diaz-RamirezEmail author
  • José Enrique Hernández-Beltrán
  • Rigoberto Juarez-Salazar
Original Research Paper


This research presents the design of a real-time system to remove the effects of haze in a sequence of monocular images. The system firstly estimates the medium transmission function from an observed hazy image using locally adaptive neighborhoods and calculation of order statistics. Next, the haze-free image is retrieved using the estimated transmission function and a physics-based restoration model. The performance of the proposed system is evaluated and compared with that of similar existing techniques in terms of objective metrics. The obtained results exhibit that the proposed system yields a higher performance in comparison with tested similar methods. Because of its high computational efficiency, the proposed system is able to operate at high rate and it is suitable for real-time applications.


Image dehazing Real-time image processing Locally adaptive neighborhoods Parallel processing Graphics processing unit 



This research was supported by Secretaría de Investigación y Posgrado - Instituto Politécnico Nacional, project SIP20171387 and Consejo Nacional de Ciencia y Tecnología, project Catedras-CONACYT-880.


  1. 1.
    Boreskov, A., Shikin, E.: Computer Graphics: From Pixels to Programmable Graphics Hardware. Chapman & Hall/CRC, London (2014)Google Scholar
  2. 2.
    Bui, T.M., Tran, H.N., Kim, W., Kim, S.: Segmenting dark channel prior in single image dehazing. Electron. Lett. 50(7), 516–518 (2014)CrossRefGoogle Scholar
  3. 3.
    Chambolle, A.: An algorithm for total variation minimization and applications. J. Math. Imaging Vis. 20(1), 89–97 (2004)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Chambolle, A., Pock, T.: A first-order primal–dual algorithm for convex problems with applications to imaging. J. Math. Imaging Vis. 40(1), 120–145 (2011)MathSciNetCrossRefGoogle Scholar
  5. 5.
    El-Hashash, M.M., Aly, H.A.: High-speed video haze removal algorithm for embedded systems. J. Real-Time Image Proc. (2016). doi: 10.1007/s11554-016-0603-1 CrossRefGoogle Scholar
  6. 6.
    Fattal, R.: Single image dehazing. ACM Trans. Graph. 27(3), 72:1–72:9 (2008)CrossRefGoogle Scholar
  7. 7.
    Gao, R., Wang, Y., Liu, M., Fan, X.: Fast algorithm for dark channel prior. Electron. Lett. 50(24), 1826–1828 (2014)CrossRefGoogle Scholar
  8. 8.
    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)CrossRefGoogle Scholar
  9. 9.
    He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)CrossRefGoogle Scholar
  10. 10.
    Kober, V., Mozerov, M., Alvarez-Borrego, J.: Nonlinear filters with spatially connected neighborhoods. Opt. Eng. 40(6), 971–983 (2001)CrossRefGoogle Scholar
  11. 11.
    Koschmieder, H.: Theorie der horizontalen sichtweite. Beitr Phys Freien Atm 12, 171–181 (1924)Google Scholar
  12. 12.
    Lee, S., Yun, S., Nam, J.H., Won, C.S., Jung, S.W.: A review on dark channel prior based image dehazing algorithms. EURASIP J. Image Video Process. 1, 4 (2016)CrossRefGoogle Scholar
  13. 13.
    Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242 (2008)CrossRefGoogle Scholar
  14. 14.
    Liu, H., Yang, J., Wu, Z., Zhang, Q.: Fast single image dehazing based on image fusion. J. Electron. Imaging 24(1), 013020 (2015)CrossRefGoogle Scholar
  15. 15.
    Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. Int. J. Comput. Vision 48(3), 233–254 (2002)CrossRefGoogle Scholar
  16. 16.
    Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25(6), 713–724 (2003)CrossRefGoogle Scholar
  17. 17.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D 60(1), 259–268 (1992)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Polarization-based vision through haze. Appl. Opt. 42(3), 511–525 (2003)CrossRefGoogle Scholar
  19. 19.
    Sun, W., Guo, B.L., Li, D.J., Jia, W.: Fast single-image dehazing method for visible-light systems. Opt. Eng. 52(9), 093103–093103 (2013)CrossRefGoogle Scholar
  20. 20.
    Tarel, J.P., Hautire, N.: Fast visibility restoration from a single color or gray level image. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2201–2208 (2009)Google Scholar
  21. 21.
    Wang, D., Zhu, J., Yan, F.: Dehazing for single image with sky region via self-adaptive weighted least squares model. Opt. Eng. 55(4), 043106 (2016)CrossRefGoogle Scholar
  22. 22.
    Wang, J.G., Tai, S.C., Lin, C.J.: Image haze removal using a hybrid of fuzzy inference system and weighted estimation. J. Electron. Imaging 24(3), 033027 (2015)CrossRefGoogle Scholar
  23. 23.
    Yang, J., Jiang, B., Lv, Z., Jiang, N.: A real-time image dehazing method considering dark channel and statistics features. J. Real-Time Image Proc. (2017). doi: 10.1007/s11554-017-0671-x CrossRefGoogle Scholar
  24. 24.
    Yu, T., Riaz, I., Piao, J., Shin, H.: Real-time single image dehazing using block-to-pixel interpolation and adaptive dark channel prior. IET Image Process 9(9), 725–734 (2015)CrossRefGoogle Scholar
  25. 25.
    Zhu, Q., Mai, J., Shao, L.: A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24(11), 3522–3533 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Instituto Politécnico Nacional - CITEDITijuanaMexico
  2. 2.CONACYT - Instituto Politécnico Nacional, CITEDITijuanaMexico

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