Single image fog and haze removal based on self-adaptive guided image filter and color channel information of sky region

Abstract

In this paper, we report an effective algorithm for removing both fog and haze from a single image. Existing algorithms based on atmospheric degeneration model generally lead to non-definite solutions for the haze and thick fog images, though they are very efficient for thin fog images. In general, as the algorithms based on vision enhancement cannot automatically adjust weight coefficient for the different structure images, the excessive or inadequate enhancement may emerge. In this paper an original degradation image is primarily segmented into the sky and non-sky regions, and then the main boundaries of non-sky region are extracted using L 0 smoothing filter. So our vision enhancement algorithm automatically adjusts weight coefficient according to various structure images. At the stage of vision enhancement, guided image filter famous for its excellent boundary preservation is adopted. As for haze image, the color channel information scattered by haze particles can be obtained in the sky region to make an effective color correction. Both the subjective and objective evaluations of experimental results demonstrate that the proposed algorithm has more outstanding recovery effect for haze and thick fog images. Moreover, the proposed algorithm can judge fog or haze image, which is a by-product of this research.

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References

  1. 1.

    Fattal R (2008) Single image dehazing. ACM Trans Graph 27(3):1–9

    Article  Google Scholar 

  2. 2.

    Fu Z, Yang Y, Shu C, Li Y, Wu H, Xu J (2015) Improved single image dehazing using dark channel prior. J Syst Eng Electron 26(5):1070–1079

    Article  Google Scholar 

  3. 3.

    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 

  4. 4.

    He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409

    Article  Google Scholar 

  5. 5.

    Jiang B, Zhang W, Ma X, Ru Y, Meng H, Wang L (2015) Method for sky region segmentation. IET Electron Lett 51(25):2104–2106

    Article  Google Scholar 

  6. 6.

    Jiang B, Zhang W, Meng H, Ru Y, Zhang Y, Ma X (2015) Single image haze removal on complex imaging background. IEEE Int Conf Softw Eng Serv Sci:280–283

  7. 7.

    Khan SH, Bennamoun M, Sohel F, Togneri R (2016) Automatic shadow detection and removal from a single image. IEEE Trans Pattern Anal Mach Intell 38(3):431–446

    Article  Google Scholar 

  8. 8.

    Li B, Wang S, Zheng L (2015) Single image haze removal using content-adaptive dark channel and post enhancement. IET Comput Vis 8(2):131–140

    Article  Google Scholar 

  9. 9.

    Mittal A, Moorthy AK, Bovik AC (2012) No-Reference Image Quality Assessment in the Spatial Domain. IEEE Trans Image Process 21(12):4695–4708

    MathSciNet  Article  MATH  Google Scholar 

  10. 10.

    Tarel JP, Hautière N (2009) Fast visibility restoration from a single color or gray level image. IEEE International Conference on computer vision: 2201–2208

  11. 11.

    J. Wang, N. He, L. Zhang, and K. Lu (2015) Single image dehazing with a physical model and dark channel prior. Neurocomputing 149(PB): 718–728

  12. 12.

    Xu L, Lu C, Xu Y, Jia J (2011) Image smoothing via L 0 gradient minimization. ACM Trans Graph 30(6):1–11

    Google Scholar 

  13. 13.

    Yu J, Tao D, Wang M (2012) Adaptive hypergraph learning and its application in image classification. IEEE Trans Image Process 21(7):3262–3272

    MathSciNet  Article  MATH  Google Scholar 

  14. 14.

    Yu T, Riaz I, Piao J, Shin H (2015) Real-time single image dehazing using block-to-pixel interpolation and adaptive dark channel prior. IET Image Process 9(9):725–734

    Article  Google Scholar 

  15. 15.

    J. Zhao, M. Gong, J. Liu, and L. Jiao (2014) Deep learning to classify difference image for image change detection. International Joint Conference on Neural Networks: 411–417

  16. 16.

    Zhu Q, Mai J, Shao L (2015) A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process 24(11):3522–3533

    MathSciNet  Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 41601353, 61503300), and Foundation of Key Laboratory of Space Active Opto-Electronics Technology of Chinese Academy of Sciences (No. AOE-2016-A02), and Scientific Research Program Funded by Shaanxi Provincial Education Department (No. 16JK1765), and Natural Science Basic Research Plan in Shaanxi Province of China (No. 2014JQ8327 and 2017JQ4003) and Foundation of State Key Laboratory of Transient Optics and Photonics, Chinese Academy of Sciences (No. SKLST201614).

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Correspondence to Bo Jiang.

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Jiang, B., Meng, H., Zhao, J. et al. Single image fog and haze removal based on self-adaptive guided image filter and color channel information of sky region. Multimed Tools Appl 77, 13513–13530 (2018). https://doi.org/10.1007/s11042-017-4973-6

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Keywords

  • Fog and haze removal
  • Color correction
  • Main boundaries extraction