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Image defogging approach based on incident light frequency

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Abstract

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.

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References

  1. Caraffa L, Tarel JP (2013) Markov random field model for single image defogging. In: IEEE Intelligent Vehicle Symposium 994–999

  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

    Article  MathSciNet  MATH  Google Scholar 

  3. Guo F, Cai Z (2012) Objective assessment method for the clearness effect of image defogging algorithm. Acta Automat Sin 38(9):1410–1419

    Article  MathSciNet  Google Scholar 

  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):2420–2426

    Article  Google Scholar 

  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):87–95

    Article  MathSciNet  MATH  Google Scholar 

  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

    Article  Google Scholar 

  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 77:13513–13530

    Article  Google Scholar 

  8. Jobson D J, Rahman Z, Woodell GA (2002) Statistics of visual representation. In: Proceedings of the 2002 Visual Information Processing XI, 25–35

  9. Levin A, Lischinski D, Weiss Y (2008) A closed-form solution to natural image matting. IEEE Trans Pattern Anal Mach Intell 30(2):228–242

    Article  Google Scholar 

  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):58–63

    Google Scholar 

  11. Li Y, Miao QG, Liu RY (2018) A multi-scale fusion scheme based on haze-relevant features for single image dehazing. Neurocomputing 283:73–86

    Article  Google Scholar 

  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):1264–1273

    Google Scholar 

  13. Narasimhan SG, Nayar SK (2002) Vision and the atmosphere. Int J Comput Vis 48(3):233–254

    Article  MATH  Google Scholar 

  14. Nayar SK, Narasimhan SG (1999) Vision in bad weather. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, 820–827

  15. Nishino K, Kratz L, Lombardi S (2012) Bayesian defogging. Int J Comput Vis 98:263–278

    Article  MathSciNet  Google Scholar 

  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, 154–169

  17. Rui Y, Li P, Sun J (2006) Images defogging techniques based on color constancy theory. J Nanjing Univ Sci Technol 30(5):622–625

    Google Scholar 

  18. Sajana MI, Muhammad NBK (2015) Review and prospect of image dehazing techniques. Int J Digit Appl Contemp Res 4(2):1–6

    Google Scholar 

  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:321–325

  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):711–724

    Article  Google Scholar 

  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):200–214

    Article  Google Scholar 

  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, 2201–2208

  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 4:6–20

  24. Wang Y, Fan C (2014) Single image defogging by multiscale depth fusion. IEEE Trans Image Process 23(11):4826–4837

    Article  MathSciNet  MATH  Google Scholar 

  25. Wang W, He C, Xia X (2018) A constrained total variation model for single image dehazing. Pattern Recogn 80:196–209

    Article  Google Scholar 

  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):4865–4880

    Article  Google Scholar 

  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 online

  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, 1596–1599

  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):3064–3072

    Article  Google Scholar 

  30. Zhang T, Chen Y (2015) Single image dehazing based on improved dark channel prior. In: ICSI-CCI 2015, Part III, LNCS 9142, 205–212

  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, 1908–1915

  32. Zhang L, Song M, Yang Y et al (2014) Weakly supervised photo cropping. IEEE Trans Multimed 16(1):94–107

    Article  Google Scholar 

  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 177–182

  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):564–571

    Article  Google Scholar 

  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):158–165

    Article  MathSciNet  Google Scholar 

  36. Zhu P, Zhu H, Qian X et al (2004) An image clearness method for fog. J Image Graph 9(1):124–128

    Google Scholar 

  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):3522–3533

    Article  MathSciNet  MATH  Google Scholar 

  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):1–9

    Google Scholar 

  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 2018:13

Download references

Acknowledgements

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.

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Correspondence to Lin Wang.

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Fan, X., Wang, L. Image defogging approach based on incident light frequency. Multimed Tools Appl 78, 17653–17672 (2019). https://doi.org/10.1007/s11042-018-7103-1

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