Image defogging approach based on incident light frequency

  • Xunli Fan
  • Lin WangEmail author


Aiming at solving the problem of color distortion existing in the dark original pruning algorithm, an improved transmittance computation approach separated for each color channel is proposed. Firstly, the influence of the incident light frequency on the transmittance of each color channel is analyzed based on Beer-Lambert law. Meanwhile, the proportional relationship among the transmittance of each channel is deduced. Secondly, the image is resumed to improve the operation efficiency. After that, the image is pretreated to get the refined transmittance. Finally, the transmittance of all the color channels is obtained through the proportional relationship. And the corresponding transmittance is used to recover the image on each channel. Thus, the image defogging is realized. We evaluate the proposed algorithm qualitatively and quantitatively. From the subjective results, the proposed algorithm has better visual effect than that of the other algorithms, and our method has more details compared to the other two methods. While from the objective results, the proposed approach can achieve natural image color without high saturation, and reduce the running time by 4 to 10 times compared with several state-of-art algorithms. The proposed algorithm can obtain a higher color fidelity and a better image color in terms of e, \( \overline{r} \) and H. The proposed method is obviously superior to those of the others in terms of no-reference quality evaluator in spatial domain and has the highest average PSNR value.


Image defogging Dark primary color prior Color distortion Transmittance Incident light frequency 



This work was supported in part by National Natural Science Foundation of China under grants 61503300 and 61801384. National Key R&D Program of China under grants 2017YFB1002804 and 2017YFB1402105, Natural Science Foundation of Shaanxi Province of China under grant 2018JM6122.


  1. 1.
    Caraffa L, Tarel JP (2013) Markov random field model for single image defogging. In: IEEE Intelligent Vehicle Symposium 994–999Google Scholar
  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):3888–3901MathSciNetCrossRefGoogle Scholar
  3. 3.
    Guo F, Cai Z (2012) Objective assessment method for the clearness effect of image defogging algorithm. Acta Automat Sin 38(9):1410–1419MathSciNetCrossRefGoogle Scholar
  4. 4.
    Hao Z, Pan D, Gong F et al (2008) Optical radiance characteristics of sea fog based on remote sensing. Acta Opt Sin 28(12):2420–2426CrossRefGoogle Scholar
  5. 5.
    Hautière N, Tarel JP, Aubert D et al (2008) Blind contrast enhancement assessment by gradient rationing at visible edges. Image Anal Stereol 27(2):87–95MathSciNetCrossRefGoogle 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):2341–2353CrossRefGoogle Scholar
  7. 7.
    Jiang B, Meng H, Zhao J et al (2017) Single image fog and haze removal based on self-adaptive guided image filter and color channel information of sky region. Multimed Tools Appl 77:13513–13530CrossRefGoogle Scholar
  8. 8.
    Jobson D J, Rahman Z, Woodell GA (2002) Statistics of visual representation. In: Proceedings of the 2002 Visual Information Processing XI, 25–35Google Scholar
  9. 9.
    Levin A, Lischinski D, Weiss Y (2008) A closed-form solution to natural image matting. IEEE Trans Pattern Anal Mach Intell 30(2):228–242CrossRefGoogle Scholar
  10. 10.
    Li Y, Chen J, Liu C et al (2006) An effective approach to remove cloud-fog cover and enhance remote sensing imagery. J Chengdu Univ Technol (Sci Technol Ed) 33(1):58–63Google Scholar
  11. 11.
    Li Y, Miao QG, Liu RY (2018) A multi-scale fusion scheme based on haze-relevant features for single image dehazing. Neurocomputing 283:73–86CrossRefGoogle Scholar
  12. 12.
    Liu H, Yang J, Wu Z et al (2015) A fast single image dehazing method based on dark channel prior and retinex theory. Acta Automat Sin 41(7):1264–1273Google Scholar
  13. 13.
    Narasimhan SG, Nayar SK (2002) Vision and the atmosphere. Int J Comput Vis 48(3):233–254CrossRefGoogle Scholar
  14. 14.
    Nayar SK, Narasimhan SG (1999) Vision in bad weather. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, 820–827Google Scholar
  15. 15.
    Nishino K, Kratz L, Lombardi S (2012) Bayesian defogging. Int J Comput Vis 98:263–278MathSciNetCrossRefGoogle Scholar
  16. 16.
    Ren W, Liu S, Zhang H, et al (2016) Single image dehazing via multi-scale convolutional neural networks. In: European Conference on Computer Vision, 154–169Google Scholar
  17. 17.
    Rui Y, Li P, Sun J (2006) Images defogging techniques based on color constancy theory. J Nanjing Univ Sci Technol 30(5):622–625Google Scholar
  18. 18.
    Sajana MI, Muhammad NBK (2015) Review and prospect of image dehazing techniques. Int J Digit Appl Contemp Res 4(2):1–6Google Scholar
  19. 19.
    Schechner YY, Narasimhan SG, Nayar RSK (2001) Instant dehazing of images using polarization. In: proceedings of IEEE conference on computer vision and. Pattern Recogn:321–325Google Scholar
  20. 20.
    Tang Z, Zhang X, Zhang S (2014) Robust perceptual image hashing based on ring partition and NMF. IEEE Trans Knowl Data Eng 26(3):711–724CrossRefGoogle Scholar
  21. 21.
    Tang Z, Zhang X, Li X et al (2016) Robust image hashing with ring partition and invariant vector distance. IEEE Trans Inform Forensic Sec 11(1):200–214CrossRefGoogle Scholar
  22. 22.
    Tarel JP, Hautiere N (2009) Fast visibility restoration from a single color or gray level image. In: Proceedings of the 12th IEEE International Conference on Computer Vision, 2201–2208Google Scholar
  23. 23.
    Tarel J-P, Hautiere N, Caraffa L, Cord A, Halmaoui H, Gruyer D (2012) Vision enhancement in homogeneous and heterogeneous fog. IEEE Intell Transp Syst Mag 4:6–20Google Scholar
  24. 24.
    Wang Y, Fan C (2014) Single image defogging by multiscale depth fusion. IEEE Trans Image Process 23(11):4826–4837MathSciNetCrossRefGoogle Scholar
  25. 25.
    Wang W, He C, Xia X (2018) A constrained total variation model for single image dehazing. Pattern Recogn 80:196–209CrossRefGoogle Scholar
  26. 26.
    Wen X, Hu D, Dong X et al (2014) An object-oriented daytime land fog detection approach based on NDFI and fractal dimension using EOS/MODIS data. Int J Remote Sens 35(13):4865–4880CrossRefGoogle Scholar
  27. 27.
    Xiong C, Xiang R, Li Y, and et al (2018) Large-scale image-based fog detection based on cloud platform. Multimedia Tools and Applications, available onlineGoogle Scholar
  28. 28.
    Yin F, Wong DWK, Quan Y, et al, (2015) A cloud-based system for automatic glaucoma screening. In: 37th Annual International Conference of IEEE Engineering in Medicine and Biology Society, 1596–1599Google Scholar
  29. 29.
    Yitzhaky Y, Dror I, Kopeika NS (1997) Restoration of atmospherically blurred images according to weather-predicted atmospheric modulation transfer functions. Opt Eng 36(11):3064–3072CrossRefGoogle Scholar
  30. 30.
    Zhang T, Chen Y (2015) Single image dehazing based on improved dark channel prior. In: ICSI-CCI 2015, Part III, LNCS 9142, 205–212Google Scholar
  31. 31.
    Zhang L, Song M, Liu Z, et al (2013) Probabilistic graphlet cut: exploring spatial structure cue for weakly supervised image segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1908–1915Google Scholar
  32. 32.
    Zhang L, Song M, Yang Y et al (2014) Weakly supervised photo cropping. IEEE Trans Multimed 16(1):94–107CrossRefGoogle Scholar
  33. 33.
    Zhang L, Li X, Hu B, and et al (2015) Research on fast smog free algorithm on single image. In: First International Conference on Computational Intelligence Theory, Systems and Applications 177–182Google Scholar
  34. 34.
    Zhang L, Gao Y, Xia Y et al (2015) A fine-grained image categorization system by cellet-encoded spatial pyramid modeling. IEEE Trans Ind Electron 62(1):564–571CrossRefGoogle Scholar
  35. 35.
    Zhao H, Xiao C, Yu J et al (2015) Single image fog removal based on local Extrema. IEEE/CAA J Auto Sin 2(2):158–165MathSciNetCrossRefGoogle Scholar
  36. 36.
    Zhu P, Zhu H, Qian X et al (2004) An image clearness method for fog. J Image Graph 9(1):124–128Google Scholar
  37. 37.
    Zhu Q, Mai J, Shao L (2015) A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process 24(11):3522–3533MathSciNetCrossRefGoogle Scholar
  38. 38.
    Zhu M, Zheng X, Zhao MH (2017) Fast single-image dehazing method based on luminance dark prior. Int J Pattern Recognit Artif Intell 31(2):1–9Google Scholar
  39. 39.
    Zhu M, Guo B, Zhao M (2018) Nighttime low illumination image enhancement with single image using bright/dark channel prior. EURASIP J Image Video Proc 2018:13Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information Science and TechnologyNorthwest UniversityXi’anChina

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