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A real-time image dehazing method considering dark channel and statistics features

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Abstract

In recent years, image dehazing algorithms are promoted, but they have not been used in real-time processing. This paper proposed a combined algorithm based on both dark channel prior and histogram optimization. First of all, the histogram optimization algorithm are used in image preprocessing, which can make the image contrast stretching, so the impact of the haze on the image can be weakened. If the obtained dehazing image can meet the requirements of the system, it will no longer be dealed with in following treatment, so we can save a lot of processing time. If it cannot meet the requirements, the dark channel prior can be used to estimate the haze intensity. According to the characteristics of the haze image, the correlation in frequency domain can be chosen. In this way, the software system can quickly deal with the images or videos to achieve real-time application requirements. Experiments show that proposed algorithm can not only meet the basic requirements for image dehazing, but also can improve the computational efficiency, so as to meet the application of real-time image processing.

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References

  1. Fang, S., Xia, X.S., Huo, X., et al.: Image dehazing using polarization effects of objects and airlight. Opt. Express 22(16), 19523–19537 (2014)

    Article  Google Scholar 

  2. Saponara, S., Fanucci, L., Petri, E.: A multi-processor NoC-based architecture for real-time image/video enhancement. J. Real Time Image Process. 8(1), 1–15 (2011)

    Article  Google Scholar 

  3. Lai, Y.H., Chen, Y.L., Chiou, C.J., et al.: Single image dehazing via optimal transmission map under scene priors. IEEE Trans. Circuits Syst. Video Technol. 25(1), 1–1 (2014)

    Google Scholar 

  4. Oakley, J.P., Satherley, B.L.: Improving image quality in poor visibility conditions using a physical model for contrast degradation. IEEE Trans. Image Process. 7(2), 167–179 (1998)

    Article  Google Scholar 

  5. Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Instant dehazing of images using polarization. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE p 325 (2001)

  6. Ancuti, C.O., Ancuti, C.: Single image dehazing by multi-scale fusion. IEEE Trans. Image Process. 22(8), 3271–3282 (2013)

    Article  Google Scholar 

  7. Yoon, I., Kim, S., Kim, D., Hayes, M.H.: Adaptive defogging with color correction in the hsv color space for consumer surveillance system. IEEE Trans. Consum. Electron. 58(1), 111–116 (2012)

    Article  Google Scholar 

  8. Shiau, Y.H., Chen, P.Y., Yang, H.Y., et al.: Weighted haze removal method with halo prevention. J. Vis. Commun. Image Represent. 25(2), 445–453 (2013)

    Article  Google Scholar 

  9. Boyer, P., Buchheit, F., Thiebaut, J.B., et al.: Haze Detection and Removal in Remotely Sensed Multispectral Imagery. IEEE Trans. Geosci. Remote Sens. 52(9), 5895–5905 (2014)

    Article  Google Scholar 

  10. Qing, C., Yu, F., Xu, X., et al.: Underwater video dehazing based on spatial Ctemporal information fusion. Multidimens. Syst. Signal Process. 27(4), 1–16 (2016)

    Article  MathSciNet  Google Scholar 

  11. Wang, Y.K., Fan, C.T.: Single image defogging by multiscale depth fusion. IEEE Trans. Image Process. A Publ. IEEE Signal Process. Soc. 23(11), 4826–4837 (2014)

    Article  MathSciNet  Google Scholar 

  12. Mudge, J., Virgen, M.: Real time polarimetric dehazing. Appl. Opt. 52(52), 1932–1938 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE pp. 1956–1963 (2009)

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

    Article  Google Scholar 

  16. Gibson, K.B., Vo, D.T., Nguyen, T.Q.: An investigation of dehazing effects on image and video coding. IEEE Trans. Image Process. A Publ. IEEE Signal Process. Soc. 21(2), 662–673 (2012)

    Article  MathSciNet  Google Scholar 

  17. Chiang, J.Y., Chen, Y.C.: Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans. Image Process. 21(4), 1756–1769 (2012)

    Article  MathSciNet  Google Scholar 

  18. Carlsohn, M.F.: Spectral image processing in real-time. J. Real Time Image Process. 1(1), 25–32 (2006)

    Article  Google Scholar 

  19. Zhou, P., Zhou, Y., Wu, D., Jin, H.: Differentially private online learning for cloud-based video recommendation with multimedia big data in social networks. IEEE Trans. Multimed. 18(6), 1217–1229 (2016)

    Article  Google Scholar 

  20. Lu, H., Li, Y., Serikawa, S.: Underwater image enhancement using guided trigonometric bilateral filter and fast automatic color correction. In: Proceedings of 20th IEEE International Conference on Image Processing (ICIP2013) pp. 3412–3416 (2013)

  21. Zhang, B., Dai, S.: Fast image haze-removal algorithm based on the prior dark-channel. J. Image Gr. 18(2), 184–188 (2013)

    MathSciNet  Google Scholar 

  22. Skounakis, E., Banitsas, K., Badii, A., Tzoulakis, S.: Atd: a multiplatform for semiautomatic 3-d detection of kidneys and their pathology in real time. IEEE Trans. Human-Machine Syst. 44(1), 146–153 (2014)

    Article  Google Scholar 

  23. Montalban, J., Zhang, L., Gil, U., Wu, Y.: Cloud transmission: system performance and application scenarios. IEEE Trans. Broadcast. 60(2), 170–184 (2014)

    Article  Google Scholar 

  24. Todorovich, E., Pra, A.L.D., Passoni, L.I., et al.: Real-time speckle image processing. J. Real Time Image Process. 11(3), 1–11 (2016)

    Article  Google Scholar 

  25. Qin, B., Huang, Z., Zeng, F., et al.: Fast single image dehazing with domain transformation-based edge-preserving filter and weighted quadtree subdivision. In: IEEE International Conference on Image Processing. IEEE (2015)

  26. Wu, C.C., Chen, H.M., Chang, C.I.: Real-time N-finder processing algorithms for hyperspectral imagery. J. Real Time Image Process. 7(2), 1–25 (2012)

    Article  Google Scholar 

  27. Zhang, J., Hu, S.: A GPU-accelerated real-time single image de-hazing method using pixel-level optimal de-hazing criterion. J. Real Time Image Process. 9(4), 661–672 (2014)

    Article  Google Scholar 

  28. Kim, J.H., Jang, W.D., Sim, J.Y., et al.: Optimized contrast enhancement for real-time image and video dehazing. J. Vis. Commun. Image Represent. 24(3), 410–425 (2013)

    Article  Google Scholar 

  29. Gao, Y., Hu, H.M., Wang, S., et al.: A fast image dehazing algorithm based on negative correction. Signal Process. 103(10), 380–398 (2014)

    Article  Google Scholar 

  30. Zhang, J., Ding, Y., Yang, Y., et al.: Real-time defog model based on visible and near-infrared information. In: IEEE International Conference on Multimedia and Expo Workshops. IEEE Computer Society, 1–6 (2016)

  31. Xiao, C., Liu, M., Xiao, D., et al.: Fast closed-form matting using a hierarchical data structure. IEEE Trans. Circuits Syst. Video Technol. 24(1), 49–62 (2014)

    Article  MathSciNet  Google Scholar 

  32. Liu, F., Yang, C.: A fast method for single image dehazing using dark channel prior. In: IEEE International Conference on Signal Processing, Communications and Computing. 483–486 (2014)

  33. Tang, K., Yang, J., Wang, J.: Investigating haze-relevant features in a learning framework for image dehazing. In: IEEE Conference on Computer Vision and Pattern Recognition 2995–3002 (2014)

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Acknowledgements

This research is partially supported by National Natural Science Foundation of China (No. 61471260 and No. 61271324), and Natural Science Foundation of Tianjin (No. 16JCYBJC16000).

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Authors and Affiliations

  1. School of Electronic Information Engineering, Tianjin University, Tianjin, People’s Republic of China

    Jiachen Yang & Bin Jiang

  2. Department of Computer Science, University College London, London, WC1E 6EA, UK

    Zhihan Lv

  3. Management services company of the first mining area, Da Gang Oilfield, PetroChina, Tianjin, People’s Republic of China

    Na Jiang

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  1. Jiachen Yang
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  2. Bin Jiang
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  4. Na Jiang
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Correspondence to Zhihan Lv.

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Yang, J., Jiang, B., Lv, Z. et al. A real-time image dehazing method considering dark channel and statistics features. J Real-Time Image Proc 13, 479–490 (2017). https://doi.org/10.1007/s11554-017-0671-x

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  • DOI: https://doi.org/10.1007/s11554-017-0671-x

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