Advertisement

Marine Snow Removal Using a Fully Convolutional 3D Neural Network Combined with an Adaptive Median Filter

  • Michał Koziarski
  • Bogusław CyganekEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11188)

Abstract

Marine snow is a type of noise that affects underwater images. It is caused by various biological and mineral particles which stick together and cause backscattering of the incident light. In this paper a method of marine snow removal is proposed. For particle detection a fully convolutional 3D neural network is trained with a manually annotated images. Then, marine snow is removed with an adaptive median filter, guided by the output of the neural network. Experimental results show that the proposed solution is capable of an accurate removal of marine snow without negatively affecting the image quality.

Keywords

Marine snow removal Underwater image processing Deep neural networks Median filtering 

Notes

Acknowledgments

This work was supported by the National Science Center NCN, Poland, under the grant no. 2016/21/B/ST6/01461. The support of the PLGrid infrastructure is also greatly appreciated.

References

  1. 1.
    Banerjee, S., Sanyal, G., Ghosh, S., Ray, R., Shome, S.N.: Elimination of marine snow effect from underwater image - an adaptive probabilistic approach. In: 2014 IEEE Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1–4. IEEE (2014)Google Scholar
  2. 2.
    Carson, R.: The Sea Around Us. Oxford University Press, Cary (1951)CrossRefGoogle Scholar
  3. 3.
    Cyganek, B.: Object Detection and Recognition in Digital Images: Theory and Practice. Wiley, Hoboken (2013)CrossRefGoogle Scholar
  4. 4.
    Farhadifard, F., Radolko, M., von Lukas, U.F.: Single image marine snow removal based on a supervised median filtering scheme. In: VISIGRAPP (4: VISAPP), pp. 280–287 (2017)Google Scholar
  5. 5.
    Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)Google Scholar
  6. 6.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  7. 7.
    Hines, C.L.: The official William Beebe web site. https://sites.google.com/site/cwilliambeebe/Home/bathysphere. Accessed 14 Mar 2018
  8. 8.
    Jaffe, J.: Underwater optical imaging: the past, the present, and the prospects. In: IEEE Journal of Oceanic Engineering, pp. 683–700. IEEE (2015)CrossRefGoogle Scholar
  9. 9.
    Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)Google Scholar
  10. 10.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  11. 11.
    Kocak, D.M., Dalgleish, F.R., Caimi, F.M., Schechner, Y.Y.: A focus on recent developments and trends in underwater imaging. Mar. Technol. Soc. J. 42(1), 52–67 (2008)CrossRefGoogle Scholar
  12. 12.
    Koziarski, M., Cyganek, B.: Image recognition with deep neural networks in presence of noise-dealing with and taking advantage of distortions. Integr. Comput.-Aided Eng. 24(4), 337–349 (2017)CrossRefGoogle Scholar
  13. 13.
    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
  14. 14.
    Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, vol. 1, p. 3 (2017)Google Scholar
  15. 15.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  16. 16.
    Orzech, J.K.; Nealson, K.: Bioluminescence of marine snow: its effect on the optical properties of the sea. In: Proceedings of SPIE 0489, Ocean Optics VII. SPIE (1984)Google Scholar
  17. 17.
    Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: ENet: a deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:1606.02147 (2016)
  18. 18.
    Riley, G.: Organic aggregates in seawater and the dynamics of their formation and utilization. Limnol. Oceanogr. 8, 372–381 (1963)CrossRefGoogle Scholar
  19. 19.
    Silver, M.: Marine snow: a brief historical sketch. Limnol. Oceanogr. Bull. 24, 5–10 (2015)CrossRefGoogle Scholar
  20. 20.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  21. 21.
    Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 4489–4497. IEEE (2015)Google Scholar
  22. 22.
    Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: Advances in Neural Information Processing Systems, pp. 341–349 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of ElectronicsAGH University of Science and TechnologyKrakówPoland

Personalised recommendations