A real-time framework for video Dehazing using bounded transmission and controlled Gaussian filter



The haze phenomenon exerts a degrading effect that decreases contrast and causes color shifts in outdoor images and videos. The presence of haze in outdoor images and videos is bothersome, unpleasant, and occasionally, even dangerous. Atmospheric light scattering (ALS) model is widely used to restore hazy images. In this model, two unknown parameters should be estimated: airlight and scene transmission. The quality of dehazed images and video frames considerably depends on those two parameters as well as on the speed and accuracy of the refinement process of the approximated scene transmission, this refinement is necessary to ensure spatial coherency of the output dehazed video. Spatial coherency should be accounted for in order to eliminate flickering artifacts usually noticed when extending single-image dehazing methods to the video scenario. Classic methods typically require high computation capacity in order to dehaze videos in real time. However, when the driver assistance context is considered, these approaches are inappropriate due to the limited resources mobile environments usually have. To address this issue, this study proposes a framework for real-time video dehazing. This framework consists of two stages: single-image dehazing using the bounded transmission (BT) method, which is utilized to dehaze single video frame in real time with high accuracy; and transmission refinement stage using a filter we call controlled Gaussian filter (CGF), which is proposed for the linear and simplified refinement of the scene transmission. To evaluate the proposed framework, three image datasets in addition to two video streams are employed. Experimental results show that the single-image stage in the proposed framework is at least seven times faster than existing methods. In addition, the analysis of variance (ANOVA) test proves that the quality of dehazed images in this stage is statistically similar to or better than those obtained using existing methods. Also, experiments show that the video stage in the proposed framework is capable of real-time video dehazing with better quality than the existing methods.


Single-image dehazing Real-time video dehazing Atmospheric light scattering model Bounded transmission Image integrals Airlight estimation 



Bounded Transmission


Scene restoration from haze and similar phenomena effects


Airlight scattering


Controlled Gaussian filter


Atmospheric light, airlight or ambient light


Red, green and blue color space


Dark channel prior


Frames per second


Images and videos captured in Malaysia


Foggy road image database


Blind referenceless image spatial quality evaluator


No-reference image quality assessment


Full-reference image quality assessment


Reduced-reference image quality assessment


Mean absolute difference


Airlight by image integral


Multivariate analysis of variance


Analysis of variance


Planar assumption


Mean opinion score


Pearson linear correlation coefficient


Spearman’s rank-order correlation coefficient


Weighted median filter


Difference of Gaussians


Mean square error


Open computer vision library


Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.


  1. 1.
    Al-Zubaidy Y, Salam RA, Abdulrahim K. (2014). Estimation of skylight value in hazy outdoor images. In 2014 World Symposium on Computer Applications & Research (WSCAR), (pp. 1–5). IEEEGoogle Scholar
  2. 2.
    Fattal R. (2008) Single image dehazing. ACM Transactions on Graphics (TOG). ACM, 72Google Scholar
  3. 3.
    Gibson KB, Vo DT, Nguyen TQ (2012) An investigation of dehazing effects on image and video coding. IEEE Transactions on Image Processing (IEEE) 21:662–673MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Hautière N, Tarel J-P, Aubert D, Dumont E (2008) Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Analysis & Stereology Journal 27(2):87–95MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    He K, Sun J (2015) Fast guided filter. Computer Vision and Pattern Recognition pp1–3Google Scholar
  6. 6.
    He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33:2341–2353CrossRefGoogle Scholar
  7. 7.
    He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35:1397–1409CrossRefGoogle Scholar
  8. 8.
    Hu Y, Wang N, Tao D, Gao X, Li X (2016) SERF: a simple, effective, robust, and fast image super-resolver from cascaded linear regression. IEEE Trans Image Process 25(9):4091–4102MathSciNetCrossRefGoogle Scholar
  9. 9.
    ITU Report BT.1082-1 (1990) Studies towards the unification of picture assessment methodology. ITU, GenevaGoogle Scholar
  10. 10.
    Jisha J, Wilsky M. (2008) Enhancement of Weather degraded video sequences using wavelet fusion. 7th IEEE International Conference on Cybemetic Intelligent Systems. 1–6Google Scholar
  11. 11.
    Kim J-H, Jang W-D, Sim J-Y, Kim C-S (2013) Optimized contrast enhancement for real-time image and video dehazing. J Vis Commun Image Represent 24(3):410–425CrossRefGoogle Scholar
  12. 12.
    Koschmieder H (1924). Theories der horizontalen sichtweite. p. 171–181Google Scholar
  13. 13.
    Lee S et al (2016) A review on dark channel prior based image dehazing algorithms. EURASIP Journal on Image and Video Processing 2016(1):1–23MathSciNetCrossRefGoogle Scholar
  14. 14.
    McCartney EJ (1976). Optics of the atmosphere: scattering by molecules and particles New York, John Wiley and Sons, Inc Geophysics 1, 421Google Scholar
  15. 15.
    Middleton W (1952) Vision through the atmosphere. University of Toronto Press, TorontoMATHGoogle Scholar
  16. 16.
    Mittal A, Moorthy AK, Bovik AC (2012) No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 21(12):4695–4708MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Narasimhan SG, Nayar SK (2003) Contrast restoration of weather degraded images. IEEE Trans Pattern Anal Mach Intell 25(6):713–724CrossRefGoogle Scholar
  18. 18.
    Pedone M, Heikkilä J. (2011) Robust airlight estimation for haze removal from a single image. 2011 I.E. Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 90–96Google Scholar
  19. 19.
    Phan T, Sohoni S, Chandler DM, Larson EC (2012) Performance-analysis-based acceleration of image quality assessment. 2012 I.E. Southwest Symposium on Image Analysis and Interpretation (SSIAI). IEEE, 81–84Google Scholar
  20. 20.
    Rec. ITU-R BT.500-13 (2012). Recommendation: Methodology for the subjective assessment of the quality of television pictures. international telecommunication unionGoogle Scholar
  21. 21.
    Shepard FD (1996) Reduced visibility due to fog on the highway. 228. Transportation Research BoardGoogle Scholar
  22. 22.
    Shiau Y-H, Yang H-Y, Chen P-Y, Chuang Y-Z (2013) Hardware implementation of a fast and efficient haze removal method. Circuits and Systems for Video Technology, IEEE Transactions on 23:1369–1374CrossRefGoogle Scholar
  23. 23.
    Tan RT (2008) Visibility in bad weather from a single image. IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE, 1–8Google Scholar
  24. 24.
    Tarel J-P, Hautiere N (2009) Fast visibility restoration from a single color or gray level image. 2009 I.E. 12th International Conference on Computer Vision. IEEE, 2201–2208Google Scholar
  25. 25.
    Tarel J-P, Hautiere N, Cord A, Gruyer D, Halmaoui H. (2010). Improved visibility of road scene images under heterogeneous fog. Intelligent Vehicles Symposium (IV), 2010 I.E. (pp. 478–485). IEEEGoogle Scholar
  26. 26.
    Tomasi C, Manduchi R (1998). Bilateral Filtering for Gray and Color Images". Sixth International Conference on Computer Vision, 1998. p. 839–846. IEEEGoogle Scholar
  27. 27.
    Viola P, Jones M (2001) Robust real-time object detection. Int J Comput Vis 4:51–52Google Scholar
  28. 28.
    VQEG (2000) final report from the video quality experts group on the validation of video quality assessment. VQEGGoogle Scholar
  29. 29.
    Wang N, Gao X, Sun L, Li J (2017) Bayesian face sketch synthesis. IEEE Trans Image Process 26(3):1264–1274MathSciNetCrossRefGoogle Scholar
  30. 30.
    Wang N, Gao X, Sun L, Li J (2017). Anchored neighborhood index for face sketch synthesis. IEEE Transactions on Circuits and Systems for Video Technology 99:1–6Google Scholar
  31. 31.
    Wilscy M, John J (2008) A novel wavelet fusion method for contrast correction and visibility enhancement of color images. Proceedings of the World Congress on Engineering 1:2–4Google Scholar
  32. 32.
    Winnemoller H, Kyprianidis JE, Olsen SC (2012) XDoG: an extended difference-of-Gaussians compendium including advanced image stylization. Comput Graph 36(6):740–753CrossRefGoogle Scholar
  33. 33.
    Zhang Q, Xu L, Jia J (2014) 100+ times faster weighted median filter (WMF). 2014 I.E. Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2830–2837Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Science and TechnologyUniversiti Sains Islam, Malaysia (USIM)Negeri SembilanMalaysia
  2. 2.Department of Computing Faculty of ArtsComputing and Creative Industry Universiti Pendidikan Sultan Idris Tanjong MalimPerakMalaysia

Personalised recommendations