Multimedia Tools and Applications

, Volume 77, Issue 3, pp 2947–2958 | Cite as

Light source point cluster selection-based atmospheric light estimation

Article

Abstract

The atmospheric light value is a critical parameter in defogging algorithms that are based on atmospheric scattering models. Any error in the atmospheric light value will impact directly on the accuracy of scattering computation and thus cause chromatic distortions in the restored images. To address this problem, this paper proposes a method that relies on clustering statistics to estimate the atmospheric light value. It starts by selecting in the original image some potential atmospheric light source points, which are grouped into point clusters using a clustering technique. From these clusters, several clusters containing candidate atmospheric light source points are selected; the points are then analyzed statistically, and the cluster containing the most candidate points is used for estimating the atmospheric light value. The mean brightness vector of the candidate atmospheric light points in the chosen point cluster is used as the estimate of the atmospheric light value, and their geometric center in the image is accepted as the location of atmospheric light. The experimental results suggest that this statistical clustering method produces more accurate atmosphere brightness vectors and light source locations. This accuracy translates to, from a subjective perspective, both a more natural defogging effect and improvements in various objective image quality indicators.

Keywords

Statistics clustering Atmospherice light Transmissivity Defogging Image quality 

References

  1. 1.
    Agaian SS, Panetta K, Grigoryan AM (2000) A new measure of image enhancement. In: IASTED international conference on signal processing & communication. Citeseer, pp 19–22. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.35.4021
  2. 2.
    Choi LK, You J, Bovik AC (2015) Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans Image Process 24(11):3888–3901. doi: 10.1109/tip.2015.2456502 MathSciNetCrossRefGoogle Scholar
  3. 3.
    Cozman F, Krotkov E (1997) Depth from scattering. In: 1997 IEEE computer society conference on computer vision and pattern recognition, 1997. Proceedings. IEEE, pp 801–806. doi: 10.1109/cvpr.1997.609419
  4. 4.
    Di W, Qing-Song Z (2015) The latest research progress of image dehazing. Acta Automat Sin 41(2):221–239Google Scholar
  5. 5.
    Hautiere N, Tarel JP, Aubert D, Dumont E et al (2008) Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal Stereol J 27(2):87–95. doi: 10.5566/ias.v27.p87-95 MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    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. doi: 10.1109/cvprw.2009.5206515 CrossRefGoogle Scholar
  7. 7.
    Levin A, Lischinski D, Weiss Y (2008) A closed-form solution to natural image matting. IEEE Trans Pattern Anal Mach Intell 30(2):228–242. doi: 10.1109/tpami.2007.1177 CrossRefGoogle Scholar
  8. 8.
    Narasimhan SG, Nayar SK (2002) Vision and the atmosphere. Int J Comput Vis 48(3):233–254. doi: 10.1023/a:1016328200723 CrossRefMATHGoogle Scholar
  9. 9.
    Narasimhan SG, Nayar SK (2003) Contrast restoration of weather degraded images. IEEE Trans Pattern Anal Mach Intell 25(6):713–724. doi: 10.1109/tpami.2003.1201821 CrossRefGoogle Scholar
  10. 10.
    Narasimhan SG, Nayar SK (2003) Interactive (de) weathering of an image using physical models. In: IEEE workshop on color and photometric methods in computer vision, France, vol 1, p 6Google Scholar
  11. 11.
    Nayar SK, Narasimhan SG (1999) Vision in bad weather. In: The proceedings of the seventh IEEE international conference on computer vision, 1999, vol 2. IEEE, pp 820–827. doi: 10.1109/iccv.1999.790306
  12. 12.
    Oakley JP, Satherley BL (1998) Improving image quality in poor visibility conditions using a physical model for contrast degradation. IEEE Trans Image Process 7(2):167–179. doi: 10.1109/83.660994 CrossRefGoogle Scholar
  13. 13.
    Schechner YY, Narasimhan SG, Nayar SK (2001) Instant dehazing of images using polarization. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition, 2001. CVPR 2001, vol 1. IEEE, pp I–325. doi: 10.1109/cvpr.2001.990493
  14. 14.
    Tang Z, Zhang X, Li X, Zhang S (2016) Robust image hashing with ring partition and invariant vector distance. IEEE Trans Inf Forensics Secur 11(1):200–214. doi: 10.1109/tifs.2015.2485163 CrossRefGoogle Scholar
  15. 15.
    Tang Z, Zhang X, Zhang S (2014) Robust perceptual image hashing based on ring partition and nmf. IEEE Trans Knowl Data Eng 26(3):711–724. doi: 10.1109/tkde.2013.45 CrossRefGoogle Scholar
  16. 16.
    Wang YK, Fan CT (2014) Single image defogging by multiscale depth fusion. IEEE Trans Image Process 23(11):4826–4837. doi: 10.1109/tip.2014.2358076 MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Zhang L, Li X, Hu B, Ren X (2015) Research on fast smog free algorithm on single image. In: 2015 first international conference on computational intelligence theory, systems and applications. CCITSA. IEEE, pp 177–182. doi: 10.1109/ccitsa.2015.55
  18. 18.
    Zhang S, Li X, Zong M, Zhu X, Cheng D (2017) Learning k for knn classification. ACM Trans Intel Syst Technol (TIST) 8(3):43Google Scholar
  19. 19.
    Zhao H, Xiao C, Yu J, Xu X (2015) Single image fog removal based on local extrema. IEEE/CAA J Automat Sin 2(2):158–165. doi: 10.1109/jas.2015.7081655 MathSciNetCrossRefGoogle Scholar
  20. 20.
    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. doi: 10.1109/tip.2015.2446191 MathSciNetCrossRefGoogle Scholar
  21. 21.
    Zhu X, Zhang L, Huang Z (2014) A sparse embedding and least variance encoding approach to hashing. IEEE Trans Image Process 23(9):3737–3750. doi: 10.1109/tip.2014.2332764 MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Zhu X, Li X, Zhang S (2016) Block-row sparse multiview multilabel learning for image classification. IEEE Trans Cybern 46(2):450–461. doi: 10.1109/tcyb.2015.2403356 CrossRefGoogle Scholar
  23. 23.
    Zhu X, Li X, Zhang S, Ju C, Wu X (2016) Robust joint graph sparse coding for unsupervised spectral feature selection. In: IEEE transactions on neural networks and learning systems. IEEE. doi: 10.1109/tnnls.2016.2521602

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Energy Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengDuChina

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