Optoelectronics Letters

, Volume 13, Issue 6, pp 452–456 | Cite as

Single image defogging based on particle swarm optimization

  • Fan Guo (郭璠)
  • Cong Zhou (周聪)
  • Li-jue Liu (刘丽珏)
  • Jin Tang (唐琎)


Due to the lack of enough information to solve the equation of image degradation model, existing defogging methods generally introduce some parameters and set these values fixed. Inappropriate parameter setting leads to difficulty in obtaining the best defogging results for different input foggy images. Therefore, a single image defogging algorithm based on particle swarm optimization (PSO) is proposed in this letter to adaptively and automatically select optimal parameter values for image defogging algorithms. The proposed method is applied to two representative defogging algorithms by selecting the two main parameters and optimizing them using the PSO algorithm. Comparative study and qualitative evaluation demonstrate that the better quality results are obtained by using the proposed parameter selection method.

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  1. [1]
    Tan R.T., Visibility in Bad Weather from a Single Image, IEEE Conference on Computer Vision and Pattern Recognition, 1 (2008).Google Scholar
  2. [2]
    Nishino K., Kratz L. and Lombardi S., International Journal of Computer Vision 98, 263 (2012).MathSciNetCrossRefGoogle Scholar
  3. [3]
    He K.M., Sun J. and Tang X.O., IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 2341 (2011).CrossRefGoogle Scholar
  4. [4]
    Tarel J.P. and Hautiere N., Fast Visibility Restoration from a Single Color or Gray Level Image, IEEE International Conference on Computer Vision, 2201 (2009).Google Scholar
  5. [5]
    Lagorio A., Grosso E. and Tistarelli M., Automatic Detection of Adverse Weather Conditions in Traffic Scenes, IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, 273 (2008).Google Scholar
  6. [6]
    Hautiere N., Tarel J.-P. and Aubert D., Towards Fog-Free in-Vehicle Vision Systems through Contrast Restoration, IEEE Conference on Computer Vision and Pattern Recognition, 2374 (2007).Google Scholar
  7. [7]
    Hautiere N., Tarel J.-P., Halmaoui H., Bremond R. and Aubert D., Machine Vision and Applications 25, 667 (2014).CrossRefGoogle Scholar
  8. [8]
    Jiji G.W. and DuraiRaj P.J., Applied Soft Computing 30, 650 (2015).CrossRefGoogle Scholar
  9. [9]
    Li Y.Y., Jiao L.C., Shang R.H. and Stolkin R, Information Sciences 294, 408 (2015).MathSciNetCrossRefGoogle Scholar
  10. [10]
    Wang Z., Bovik A.C., Sheikh H.R. and Simoncelli E.P., IEEE Trans. Image Processing 13, 600 (2004).ADSCrossRefGoogle Scholar
  11. [11]
    Ji Z.X., Chen Q., Sun Q.S. and Xia D.D., Information Processing Letters 109, 1238 (2009).MathSciNetCrossRefGoogle Scholar
  12. [12]
    He K.M., Single Image Haze Removal using Dark Channel Prior, Ph.D. dissertation, The Chinese University of Hong Kong, 2011.Google Scholar
  13. [13]
    Guo F., Tang J. and Cai Z.X., J. Cent. South Univ. 21, 272 (2014).CrossRefGoogle Scholar
  14. [14]
    Kennedy J. and Eberhart R.C., Particle Swarm Optimization, International Conference Neural Network, 1942 (1995).Google Scholar

Copyright information

© Tianjin University of Technology and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Fan Guo (郭璠)
    • 1
  • Cong Zhou (周聪)
    • 1
  • Li-jue Liu (刘丽珏)
    • 1
  • Jin Tang (唐琎)
    • 1
  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina

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