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A Unified Framework of Single Image Haze Removal under Different Weather Conditions

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Intelligent Data Analysis and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 370))

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Abstract

Outdoor images are usually affected by haze, fog and smoke which are such phenomena due to atmospheric scatting, so that the degraded images suffer from the loss of contrast and color fidelity. In this paper, we summed up a variety of haze removal techniques which are grouped into two categories: physics based and non physics based, and processed some hazy images under different weather conditions. Then, we make a comparison among these results, and analyze their advantages and shortcomings, estimate the appropriate haze removed technique for each weather condition. Finally, we lay further discussions on technical challenges and future development.

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References

  1. Tan RT (2008) In: IEEE conference on computer vision and pattern recognition, CVPR 2008 (IEEE, 2008), pp 1–8

    Google Scholar 

  2. Fattal R (2008) In: ACM transactions on graphics (TOG), vol 27 (ACM, 2008), p 72

    Google Scholar 

  3. He K, Sun J, Tang X (2011) IEEE transactions on pattern analysis and machine intelligence. 33(12):2341

    Google Scholar 

  4. He K, Sun J, Tang X (2010) In: Computer vision-ECCV 2010 (Springer, 2010), pp 1–14

    Google Scholar 

  5. Tarel JP, Hautiere N (2009) In: 2009 IEEE 12th international conference on computer vision, (IEEE, 2009), pp 2201–2208

    Google Scholar 

  6. Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, ter Haar Romeny B, Zimmerman JB, Zuiderveld K (1987) Computer vision, graphics, and image processing 39(3):355

    Google Scholar 

  7. Zuiderveld K (1994) In: Graphics gems IV. Academic Press Professional Inc, pp 474–485

    Google Scholar 

  8. Land EH (1986) Proceedings of the national academy of sciences 83(10):3078

    Google Scholar 

  9. Land EH (1983) Proceedings of the national academy of sciences of the United States of America 80(16):5163

    Google Scholar 

  10. Frankle JA, McCann JJ (1983) Method and apparatus for lightness imaging. US Patent 4,384,336

    Google Scholar 

  11. McCann J (1999) In: Color and imaging conference, vol 1999. Society for Imaging Science and Technology, pp 1–8

    Google Scholar 

  12. Jobson DJ, Rahman ZU, Woodell GA (1997) IEEE transactions on image processing. 6(3):451

    Google Scholar 

  13. Rahman ZU, Jobson DJ, Woodell GA (1996) In: Proceedings of international conference on image processing, vol 3 (IEEE, 1996), pp 1003–1006

    Google Scholar 

  14. Jobson DJ, Rahman ZU, Woodell GA (1997) IEEE transactions on image processing. 6(7):965

    Google Scholar 

  15. Narasimhan SG, Nayar SK (2002) International journal of computer vision 48(3):233

    Google Scholar 

  16. Koschmieder H (1925) Theorie der horizontalen Sichtweite: Kontrast und Sichtweite. Keim & Nemnich

    Google Scholar 

  17. Draper NR, Smith H, Pownell E (1966) Applied regression analysis, vol 3. Wiley, New York

    Google Scholar 

  18. Liu Q, Chen M, Zhou D (2013) In: 25th Chinese control and decision conference (CCDC), (IEEE, 2013), pp. 3780–3785

    Google Scholar 

  19. Land EH (1986) Vision research 26(1):7

    Google Scholar 

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Correspondence to Qiang Su .

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© 2015 Springer International Publishing Switzerland

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Li, YF., Su, Q., Pan, JS., Li, JB., Cui, W., Liu, HY. (2015). A Unified Framework of Single Image Haze Removal under Different Weather Conditions. In: Abraham, A., Jiang, X., Snášel, V., Pan, JS. (eds) Intelligent Data Analysis and Applications. Advances in Intelligent Systems and Computing, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-319-21206-7_33

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  • DOI: https://doi.org/10.1007/978-3-319-21206-7_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21205-0

  • Online ISBN: 978-3-319-21206-7

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