Skip to main content
SpringerLink
Log in

Single image dehazing through improved atmospheric light estimation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Image contrast enhancement for outdoor vision is important for smart car auxiliary transport systems. The video frames captured in poor weather conditions are often characterized by poor visibility. Most image dehazing algorithms consider to use a hard threshold assumptions or user input to estimate atmospheric light. However, the brightest pixels sometimes are objects such as car lights or streetlights, especially for smart car auxiliary transport systems. Simply using a hard threshold may cause a wrong estimation. In this paper, we propose a single optimized image dehazing method that estimates atmospheric light efficiently and removes haze through the estimation of a semi-globally adaptive filter. The enhanced images are characterized with little noise and good exposure in dark regions. The textures and edges of the processed images are also enhanced significantly.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Ahlen J, Sundgren D, Bengtsson E (2007) Application of underwater hyperspectal data for color correction purposes. Pattern Recog Image Anal 17(1):170–173

    Article  Google Scholar 

  2. C. Ancuti, C. O. Ancuti, T. Haber, P. Bekaert, “Enhancing underwater images and videos by fusion”, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 81–88, 2012.

  3. F. Cozman, E. Krotkov, “Depth from scattering”, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp.801–806, 1997.

  4. Fattal R (2008) Single image dehazing. ACM Trans Graph 27(3):1–8

    Article  Google Scholar 

  5. Fattal R (2014) Dehazing using color-lines. ACM Trans Graph:1–10

  6. Gibson KB, Vo DT, Nguyen TQ (2012) An investigation of dehazing effects on image and video coding. IEEE Trans Image Process 21(2):662–673

    Article  MathSciNet  Google Scholar 

  7. N. Hautiere, J.P. Tarel, D. Aubert, “Towards fog-free in-vehicle vision systems through contrast restoration”, in IEEE Conference on Computer Vision and Pattern Recognition, pp.1–8, 2008.

  8. Hautiere N, Tarel J-P, Aubert D, Dumont E (2008) Blind contrast restoration assessment by gradient radioing at visible edges. In Proc Int Congr Stereology 27(2):87–95

    MathSciNet  MATH  Google Scholar 

  9. He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353

    Article  Google Scholar 

  10. Kim J, Jang W, Sim J, Kim C (2013) Optimized contrast enhancement for real-time image and video dehazing. J Vis Commun Image Representation 24:410–425

    Article  Google Scholar 

  11. Koschmieder H (1925) Theorie der horizontalen sichtweite: kontrast und sichtweite. Keim Nemnich Press 2:1–11

    Google Scholar 

  12. Liang J, Ren L, Ju H, Qu E, Wang Y (2014) Visibility enhancement of haze images based on a universal polarimetric imaging method. J Appl Phys 116(17):1731071–1731077

    Article  Google Scholar 

  13. Mantiuk R, Kim KJ, Rempel AG, Heidrich W (2011) HDR-VDP2: a calibrated visual metric for visibility and quality predictions in all luminance conditions. ACM Trans Graph 30(4):40–52

    Article  Google Scholar 

  14. Narasimhan S, Nayar S (2003) Contrast restoration of weather degraded images. IEEE Trans Pattern Anal Mach Intell 25(6):713–724

    Article  Google Scholar 

  15. Schechner Y, Karpel N (2005) Recovery of underwater visibility and structure by polarization analysis. IEEE J Ocean Eng 30(3):570–587

    Article  Google Scholar 

  16. Schechner Y, Narasimhan S, Nayar S (2001) “Instant dehazing of images using polarization”, in: proc. of computer vision and. Pattern Recogn 1:325–332

    Google Scholar 

  17. Schettini R, Corchs S (2010) Underwater image processing: state of the art of restoration and image enhancement methods. EURASIP J Adv Signal Process 746052

  18. Serikawa S, Lu H (2014) Underwater image dehazing using joint trilateral filter. Comput Electr Eng 40(1):41–50

    Article  Google Scholar 

  19. R.T. Tan, “Visibility in bad weather from a single image”, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp.1–8, 2008.

  20. J. Tarel, N. Hautiere, “Fast visibility restoration from a single color or gray level image”, in Proc. of the IEEE International Conference on Computer Vision, pp. 2201–2208, 2009.

  21. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  22. S. Yao, W. Lin, E. Ong, Z. Lu, “Contrast signal to noise ratio for image quality assessment”, in Proc. of International Conference on Image Processing, pp. 397–400, 2005.

Download references

Acknowledgments

All of the authors have the same contribution to this paper. This work was supported by Grant in Aid for Foreigner Research Fellows of Japan Society for the Promotion of Science (No.15F15077), Open Fund of the Key Laboratory of Marine Geology and Environment in Chinese Academy of Sciences (No.MGE2015KG02), Research Fund of State Key Laboratory of Marine Geology in Tongji University (MGK1407), Research Fund of State Key Laboratory of Ocean Engineering in Shanghai Jiaotong University (OEK1315), and Grant in Aid for Research Fellows of Japan Society for the Promotion of Science (No.13 J10713).

Author information

Authors and Affiliations

  1. Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu, Yujie Li & Seiichi Serikawa

  2. Chinese Academy of Sciences, Qingdao, China

    Huimin Lu

  3. Shanghai Jiaotong University, Shanghai, China

    Huimin Lu

  4. Yamaguchi University, Yamaguchi, Japan

    Shota Nakashima

Authors
  1. Huimin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yujie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shota Nakashima
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Seiichi Serikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huimin Lu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, H., Li, Y., Nakashima, S. et al. Single image dehazing through improved atmospheric light estimation. Multimed Tools Appl 75, 17081–17096 (2016). https://doi.org/10.1007/s11042-015-2977-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-2977-7

Keywords

Search

Navigation