Single Image Marine Snow Removal based on a Supervised Median Filtering Scheme

Fahimeh Farhadifard, Martin Radolko, Uwe Freiherr von Lukas

2017

Abstract

Underwater image processing has attracted a lot of attention due to the special difficulties at capturing clean and high quality images in this medium. Blur, haze, low contrast and color cast are the main degradations. In an underwater image noise is mostly considered as an additive noise (e.g. sensor noise), although the visibility of underwater scenes is distorted by another source, termed marine snow. This signal disturbs image processing methods such as enhancement and segmentation. Therefore removing marine snow can improve image visibility while helping advanced image processing approaches such as background subtraction to yield better results. In this article, we propose a simple but effective filter to eliminate these particles from single underwater images. It consists of different steps which adapt the filter to fit the characteristics of marine snow the best. Our experimental results show the success of our algorithm at outperforming the existing approaches by effectively removing this phenomenon and preserving the edges as much as possible.

References

  1. Abreu, E., Lightstone, M., Mitra, S. K., and Arakawa, K. (1996). A new efficient approach for the removal of impulse noise from highly corrupted images. IEEE Transactions on Image Processing, pages 1012-1025.
  2. Ancuti, C., Ancuti, C., Haber, T., and Bekaert, P. (2012). Enhancing underwater images and videos by fusion. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 81-88.
  3. Arnold-Bos, A., Malkasse, J.-P., and Kervern, G. (2005). A preprocessing framework for automatic underwater images denoising. In European Conference on Propagation and Systems.
  4. Bazeille, S., Quidu, I., Jaulin, L., and Malkasse, J.-P. (2006). Automatic underwater image pre-processing. In CMM'06.
  5. Bergman, R., Maurer, R., Nachlieli, H., Ruckenstein, G., Chase, P., and Greig, D. (2007). Comprehensive solutions for removal of dust and scratches from images.
  6. Chiang, J. and Chen, Y.-C. (2012). Underwater image enhancement by wavelength compensation and dehazing. Image Processing, IEEE Transactions on, pages 1756-1769.
  7. Gutzeit, E., Ohl, S., Kuijper, A., Voskamp, J., and Urban, B. (2010). Setting graph cut weights for automatic foreground extraction in wood log images. In VISAPP 2010, pages 60-67.
  8. Hwang, H. and Haddad, R. A. (1995). Adaptive median filters: new algorithms and results. IEEE Transactions on Image Processing, pages 499-502.
  9. Radolko, M., Farhadifard, F., and von Lukas, U. F. Dataset on underwater change detection. to appear in 2016 Oceans - Monterey.
  10. Shanmugasundaram, M., Sukumaran, S., and Shanmugavadivu, N. (2013). Fusion based denoise-engine for underwater images using curvelet transform. In Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on, pages 941-946.
  11. Slade, W. H., Boss, E., and Russo, C. (20011). Effects of particle aggregation and disaggregation on their inherent optical properties. Optics express, 19(9):7945- 7959.
  12. Srinivasan, K. and Ebenezer, D. (2007). A new fast and efficient decision-based algorithm for removal of highdensity impulse noises. IEEE signal processing letters, pages 189-192.
  13. Trucco, E. and Olmos-Antillon, A. T. (2006). Self-tuning underwater image restoration. Oceanic Engineering, IEEE Journal, pages 511-519.
  14. Wang, Z. and Zhang, D. (1999). Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, pages 78-80.
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Paper Citation


in Harvard Style

Farhadifard F., Radolko M. and Freiherr von Lukas U. (2017). Single Image Marine Snow Removal based on a Supervised Median Filtering Scheme . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 280-287. DOI: 10.5220/0006261802800287


in Bibtex Style

@conference{visapp17,
author={Fahimeh Farhadifard and Martin Radolko and Uwe Freiherr von Lukas},
title={Single Image Marine Snow Removal based on a Supervised Median Filtering Scheme},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={280-287},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006261802800287},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Single Image Marine Snow Removal based on a Supervised Median Filtering Scheme
SN - 978-989-758-225-7
AU - Farhadifard F.
AU - Radolko M.
AU - Freiherr von Lukas U.
PY - 2017
SP - 280
EP - 287
DO - 10.5220/0006261802800287