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A Deep Learning Approach for Underwater Image Enhancement

  • Javier PerezEmail author
  • Aleks C. Attanasio
  • Nataliya Nechyporenko
  • Pedro J. Sanz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10338)

Abstract

Image processing in underwater robotics is one of the most challenging problems in autonomous underwater robotics due to light transmission in water. Although image restoration techniques are able to correctly remove the haze in a degraded image they need many images from the same location making impossible to use it in a real time system. Taking into account the great results of deep learning techniques in other image processing problems such as colorizing images or detecting objects a deep learning solution is proposed. A convolutional neural network is trained with image restoration techniques to dehaze single images outperforming other image enhancement techniques. The proposed approach is able to produce image restoration quality images with a single image as input. The neural network is validated using images from different locations and characteristics to prove the generalization capabilities.

Keywords

Underwater robotics Deep learning Image dehazing 

Notes

Acknowledgments

This work has been partially funded by Spanish Ministry under grant DPI2014-57746-C3 (MERBOTS Project), Generalitat Valenciana grant PROMETEO/2016/066 and Universitat Jaume I grant PREDOC/2012/47. The authors would like to acknowledge the Australian Centre for Field Robotics’ marine robotics group for providing the data used in this work.

References

  1. 1.
    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 (2016)CrossRefGoogle Scholar
  2. 2.
    Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: Dehazenet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187–5198 (2016)MathSciNetCrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    De Novi, G., Melchiorri, C., García, J., Sanz, P., Ridao, P., Oliver, G.: A new approach for a reconfigurable autonomous underwater vehicle for intervention. In: 2009 3rd Annual IEEE Systems Conference, pp. 23–26. IEEE (2009)Google Scholar
  5. 5.
    Deng, L.: The MNIST database of handwritten digit images for machine learning research. IEEE Sig. Process. Mag. 29(6), 141–142 (2012)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Drews, P., Nascimento, E., Moraes, F., Botelho, S., Campos, M.: Transmission estimation in underwater single images. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 825–830 (2013)Google Scholar
  7. 7.
    Garg, R., Mittal, B., Garg, S.: Histogram equalization techniques for image enhancement. Int. J. Electron. Commun. Technol. 2(1), 107–111 (2011)Google Scholar
  8. 8.
    Getreuer, P.: Automatic color enhancement (ACE) and its fast implementation. Image Process. On Line 2, 266–277 (2012)CrossRefGoogle Scholar
  9. 9.
    Ghani, A.S.A., Isa, N.A.M.: Underwater image quality enhancement through integrated color model with rayleigh distribution. Appl. Soft Comput. 27, 219–230 (2015)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Sig. Process. Mag. 29(6), 82–97 (2012)CrossRefGoogle Scholar
  12. 12.
    Hitam, M.S., Awalludin, E.A., Yussof, W.N.J.H.W., Bachok, Z.: Mixture contrast limited adaptive histogram equalization for underwater image enhancement. In: 2013 International Conference on Computer Applications Technology (ICCAT), pp. 1–5. IEEE (2013)Google Scholar
  13. 13.
    Hussain, F., Jeong, J.: Visibility enhancement of scene images degraded by foggy weather conditions with deep neural networks. J. Sens. 16, 1–9 (2016)CrossRefGoogle Scholar
  14. 14.
    Iqbal, K., Abdul Salam, R., Osman, M., Talib, A.Z., et al.: Underwater image enhancement using an integrated colour model. IAENG Int. J. Comput. Sci. 32(2), 239–244 (2007)Google Scholar
  15. 15.
    Jaffe, J.S.: Computer modeling and the design of optimal underwater imaging systems. IEEE J. Oceanic Eng. 15(2), 101–111 (1990)CrossRefGoogle Scholar
  16. 16.
    Jaffe, J.S.: Enhanced extended range underwater imaging via structured illumination. Opt. Express 18(12), 12328–12340 (2010)CrossRefGoogle Scholar
  17. 17.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  18. 18.
    Mai, J., Zhu, Q., Wu, D., Xie, Y., Wang, L.: Back propagation neural network dehazing. In: 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1433–1438. IEEE (2014)Google Scholar
  19. 19.
    McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D., Barton, D.: Big data. The management revolution. Harvard Bus. Rev. 90(10), 61–67 (2012)Google Scholar
  20. 20.
    Raimondo, S., Silvia, C.: Underwater image processing: state of the art of restoration and image enhancement methods. EURASIP J. Adv. Sig. Process. 2010, 746052 (2010)Google Scholar
  21. 21.
    Roser, M., Dunbabin, M., Geiger, A.: Simultaneous underwater visibility assessment, enhancement and improved stereo. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 3840–3847. IEEE (2014)Google Scholar
  22. 22.
    Tang, K., Yang, J., Wang, J.: Investigating haze-relevant features in a learning framework for image dehazing. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2995–3002. IEEE (2014)Google Scholar
  23. 23.
    Torres-Méndez, L.A., Dudek, G.: Color correction of underwater images for aquatic robot inspection. In: Rangarajan, A., Vemuri, B., Yuille, A.L. (eds.) EMMCVPR 2005. LNCS, vol. 3757, pp. 60–73. Springer, Heidelberg (2005). doi: 10.1007/11585978_5 CrossRefGoogle Scholar
  24. 24.
    Treibitz, T., Schechner, Y.Y.: Active polarization descattering. IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 385–399 (2009)CrossRefGoogle Scholar
  25. 25.
    Vasilescu, I., Detweiler, C., Rus, D.: Color-accurate underwater imaging using perceptual adaptive illumination. Auton. Robots 31(2–3), 285–296 (2011)CrossRefGoogle Scholar
  26. 26.
    Williams, S.B., Pizarro, O.R., Jakuba, M.V., Johnson, C.R., Barrett, N.S., Babcock, R.C., Kendrick, G.A., Steinberg, P.D., Heyward, A.J., Doherty, P.J., et al.: Monitoring of benthic reference sites: using an autonomous underwater vehicle. IEEE Robot. Autom. Mag. 19(1), 73–84 (2012)CrossRefGoogle Scholar
  27. 27.
    Zhu, Q., Mai, J., Shao, L.: A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24(11), 3522–3533 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Javier Perez
    • 1
    Email author
  • Aleks C. Attanasio
    • 1
  • Nataliya Nechyporenko
    • 1
  • Pedro J. Sanz
    • 1
  1. 1.Department of Computer Science and EngineeringJaume I UniversityCastellón de la PlanaSpain

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