Skip to main content
Log in

Efficient fog removal from video

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

In this paper, a framework of real-time video processing for fog removal using uncalibrated single camera system is proposed. Intelligent use of temporal redundancy present in video frames paves the way for real-time implementation. Any fog removal algorithm for images acquired with uncalibrated single camera system can be extended to video using the proposed framework. For the purpose of real-time implementation, several fog removal algorithms for images are investigated and few top ranking algorithms in speed and quality are chosen. Simulation results confirm that proposed framework reduces the computation per frame significantly. Proposed fog removal framework has a wide application in navigation, transportation, and other industries.

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

Access this article

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. U.S. Department of Transportation Federal Highway Administration. http://ops.fhwa.dot.gov/Weather/

  2. National Highway Traffic Safety Administration. http://www.nhtsa.gov/

  3. Siogkas, G.K., Dermatas, E.S.: Detection, tracking and classification of road signs in adverse conditions. In: IEEE MELECON, pp. 537–540 (2006)

  4. Garg, K., Nayar, S.K.: Vision and rain. Int. J. Comput. Vis. 75(1), 3–27 (2007)

    Article  Google Scholar 

  5. Roser, M., Moosmann, F.: Classification of weather situations on single color images. In: IEEE Intelligent Vehicles Symposium, pp. 798–803. Eindhoven (2008)

  6. Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. Int. J. Comput. Vis. 48(3), 233–254 (2002)

    Google Scholar 

  7. Narasimhan, S.G., Nayar, S.K.: Shedding light on the weather. In: International Conference on Computer Vision and, Pattern Recognition, pp. 665–672 (2003)

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

    Article  Google Scholar 

  9. Narasimhan, S.G., Nayar, S.K.: Interactive (De) weathering of an image using physical models. In: IEEE Workshop on Color and Photometric Methods in Computer Vision, in conjunction with ICCV (2003)

  10. Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Instant dehazing of images using polarization. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 325–332 (2001)

  11. Narasimhan, S.G., Nayar, S.K.: Chromatic framework for vision in bad weather. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 598–605 (2000)

  12. He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1956–1963 (2009)

  13. Fattal, R.: Single image dehazing. In: International Conference on Computer Graphics and Interactive Techniques archive ACM SIGGRAPH, pp. 1–9 (2008)

  14. Kopf, J., Neubert, B., Chen, B., Cohen-Or, D., Deussen, O., Uyttendaele, D.: Deep photo: Model-based photograph enhancement and viewing. ACM Trans. Graph. 27(5), 116:1–116:10 (2008)

    Article  Google Scholar 

  15. Tarel, J.P., Hautiere, N.: Fast visibility restoration from a single color or gray level image. In: IEEE International Conference on Computer Vision, pp. 2201–2208 (2009)

  16. Zhang, J., Li, L., Yang, G., Zhang, Y., Sun, J.: Local albedo-insensitive single image dehazing. Vis. Comput. 26(6–8), 761–768 (2010)

    Google Scholar 

  17. Tripathi, A. K., Mukhopadhyay, S.: Single image fog removal using anisotropic diffusion. IET Image Process. (in press)

  18. Tekalp, A.M.: Digital Video Processing Englewood Cliffs, NJ: Prentice-Hall (1995)

  19. Hoelzer, S.: A Technical Report on MPEG-2 Coding. University of Illinois at Chicago (UIC), Chicago (2005)

    Google Scholar 

  20. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Reading (1992)

    Google Scholar 

  21. Economopoulosa, T.L., Asvestasa, P.A., Matsopoulos, G.K.: Contrast enhancement of images using partitioned iterated function systems. Image Vis. Comput. 28(1), 45–54 (2010)

    Article  Google Scholar 

  22. Hautiere, N., Tarel, J.P., Aubert, D., Dumont, E.: Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal. Stereol. J. 27(2), 87–95 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  23. Rapantzikos, K.E.: Dense estimation of optical flow in the compressed domain using robust techniques, M. Sc. Report, Department of Electronic& Computer Engineering, Technical University of Crete, 2002

  24. Wang, Z., Sheikh, H.R., Bovik, A.C.: No-reference perceptual quality assessment of JPEG compressed images. In: Proceedings of the IEEE International Conference on Image Processing, vol. 1, pp. 477–480 (2002)

  25. Nicolis, C.R., Nicolis, G.: Stochastic resonance. Scholarpedia 2(11), 1474 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abhishek Kumar Tripathi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tripathi, A.K., Mukhopadhyay, S. Efficient fog removal from video. SIViP 8, 1431–1439 (2014). https://doi.org/10.1007/s11760-012-0377-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-012-0377-2

Keywords

Navigation