Underwater Image Restoration Based on Red Channel and Haze-Lines Prior

  • Dabing Yu
  • Guanying HuoEmail author
  • Yan Liu
  • Yan Zhou
  • Jinxing Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11741)


Due to the scattering and absorption of light while it propagates in the water, underwater images often suffer from low contrast and color distortion. In order to solve this problem, we propose an underwater image restoration algorithm based on red channel and haze-lines prior in this paper. Firstly, the red channel prior is used to estimate veiling-light. Secondly, according to the characteristics of red channel attenuation in water, the attenuation ratio of red-blue channel and red-green channel are introduced to estimate the transmission by using haze-lines prior. Finally, the transmission is corrected by the red channel boundary constraint. In addition, for underwater artificial illumination, we introduce saturation as the low bound of the transmission estimation to reduce the impact of artificial light. The experimental results show that the proposed algorithm can restore image color information, improve image clarity and obtain better visual quality. The quantitative analysis indicates that the proposed algorithm performs well on a wide variety of underwater images and is competitive with other state-of-the-arts.


Underwater image restoration Red channel Haze-lines Saturation Artificial illumination Transmission 



This work was partially supported by the National Key R&D Program of China (2018YFC0406903), the National Natural Science Foundation of China (No. 41706103) and the Natural Science Foundation of Jiangsu (No. BK20170306).


  1. 1.
    Jaffe, J.S.: Computer modeling and the design of optimal underwater imaging systems. IEEE J. Oceanic Eng. 15(2), 101–111 (1990)CrossRefGoogle Scholar
  2. 2.
    Hou, W., Gray, D.J., Weidemann, A.D., et al.: Automated underwater image restoration and retrieval of related optical properties. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 1889–1892 (2008)Google Scholar
  3. 3.
    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)CrossRefGoogle Scholar
  4. 4.
    Sathya, R., Bharathi, M., Dhivyasri, G.: Underwater image enhancement by dark channel prior. In: International Conference on Electronics and Communication Systems, pp. 1119–1123 (2015)Google Scholar
  5. 5.
    Wen, H., Tian, Y., Huang, T., et al.: Single underwater image enhancement with a new optical model. In: IEEE International Symposium on Circuits and Systems, pp. 753–756 (2013)Google Scholar
  6. 6.
    Chiang, J.Y., Chen, Y.C.: Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans. Image Process. 21(4), 1756–1769 (2012)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Galdran, A., Pardo, D., Picon, A., et al.: Automatic red-channel underwater image restoration. J. Vis. Commun. Image Represent. 26(1), 132–145 (2015)CrossRefGoogle Scholar
  8. 8.
    Berman, D., Treibitz, T., Avidan, S.: Non-local image dehazing. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1674–1682 (2016)Google Scholar
  9. 9.
    Berman, D., Treibitz, T., Avidan, S.: Diving into haze-lines: color restoration of underwater images. In: Proceedings of the British Machine Vision Conference (BMVC), vol. 1, no. 2 (2017)Google Scholar
  10. 10.
    Berman, D., Levy, D., Avidan, S., et al.: Underwater single image color restoration using haze-lines and a new quantitative dataset. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), arxiv:1811.01343 (2018)
  11. 11.
    Jerlov, N.G.: Marine Optics, vol. 14. Elsevier, Amsterdam (1976)CrossRefGoogle Scholar
  12. 12.
    Park, D., Han, D.K., Jeon, C., Ko, H.: Fast single image dehazing using characteristics of RGB channel of foggy image. IEICE Trans. Inform. Syst. 96(8), 1793–1799 (2013)CrossRefGoogle Scholar
  13. 13.
    Bryson, M., Johnson-Roberson, M., Pizarro, O., Williams, S.B.: True color correction of autonomous underwater vehicle imagery. J. Field Robot. 33(6), 853–874 (2015)CrossRefGoogle Scholar
  14. 14.
    Drews, P.L.J., Nascimento, E.R., Botelho, S.S.C., et al.: Underwater depth estimation and image restoration based on single images. IEEE Comput. Graph. Appl. 36(2), 24–35 (2016)CrossRefGoogle Scholar
  15. 15.
    Jiang, Z.X., Pu, Y.: Underwater image color compensation based on electromagnetic theory. Laser Optoelectron. Prog. 55(08), 237–242 (2018)Google Scholar
  16. 16.
    Xie, H.L., Peng, G.H., Wang, F., et al.: Underwater image restoration based on background light estimation and dark channel prior. Acta Opt. Sinica 38(01), 18–27 (2018)Google Scholar
  17. 17.
    Yang, M., Sowmya, A.: An underwater color image quality evaluation metric. IEEE Trans. Image Process. 24(12), 6062–6071 (2015)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Panetta, K., Gao, C., Agaian, S.: Human-visual-system-inspired underwater image quality measures. IEEE J. Oceanic Eng. 41(3), 541–551 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Dabing Yu
    • 1
  • Guanying Huo
    • 1
    Email author
  • Yan Liu
    • 1
  • Yan Zhou
    • 1
    • 2
  • Jinxing Xu
    • 1
  1. 1.College of Internet of Things EngineeringHohai UniversityChangzhouChina
  2. 2.Changzhou Key Laboratory of Sensor Networks and Environmental SensingChangzhouChina

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