single-rb.php

JRM Vol.30 No.5 pp. 781-790
doi: 10.20965/jrm.2018.p0781
(2018)

Paper:

Visibility Enhancement for Underwater Robots Based on an Improved Underwater Light Model

Xiaorui Qiao*, Yonghoon Ji**, Atsushi Yamashita*, and Hajime Asama*

*The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

**Chuo University
1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan

Received:
December 20, 2017
Accepted:
July 14, 2018
Published:
October 20, 2018
Keywords:
underwater robot, image enhancement, dehaze
Abstract
Visibility Enhancement for Underwater Robots Based on an Improved Underwater Light Model

Underwater image degraded by haze and floating particles

We propose an underwater image enhancement algorithm for improving underwater robot visibility. Images captured in underwater environments are typically degraded by the effects of absorption, scattering, and noise. Degraded images impede underwater robot task performance (e.g., inspection, detection, and visual simultaneous localization and mapping). In this study, we improve the underwater light model by considering floating particle noise and non-uniform illumination from artificial light sources. Specifically, a systematic underwater enhancement method that includes a floating particle removal algorithm and an image-dehazing algorithm is proposed. Our method is effective for underwater image enhancement applications in real-world scenarios. We compare and evaluate our proposed method with state-of-the-art methods, with an underwater evaluation and a feature-matching performance. The experimental results show that our method yields comparable (and even better) results than state-of-the-art methods.

Cite this article as:
X. Qiao, Y. Ji, A. Yamashita, and H. Asama, “Visibility Enhancement for Underwater Robots Based on an Improved Underwater Light Model,” J. Robot. Mechatron., Vol.30, No.5, pp. 781-790, 2018.
Data files:
References
  1. [1] M. Takagi, H. Mori, A. Yimit, Y. Hagihara, and T. Miyoshi, “Development of a Small Size Underwater Robot for Observing Fisheries Resources – Underwater Robot for Assisting Abalone Fishing –,” J. Robot. Mechatron., Vol.28, No.3, pp. 397-403, 2016.
  2. [2] Y. Nishida, T. Ura, T. Nakatani, T. Sakamaki, J. Kojima, Y. Itoh, and K. Kim, “Autonomous Underwater Vehicle “Tuna-Sand” for Image Observation of the Seafloor at a Low Altitude,” J. Robot. Mechatron., Vol.26, No.4, pp. 519-521, 2014.
  3. [3] M. Jonaz and G. R. Fournier, “Light scattering by particles in water: theoretical and experimental foundations,” Academic Press, 2007.
  4. [4] K. He, J. Sun, and X. Tang, “Single Image Haze Removal Using Dark Channel Prior,” IEEE Trans. on Pattern Analysis and Machine Intell., Vol.32, No.12, pp. 2341-2353, 2011.
  5. [5] C. O. Ancuti and C. Ancuti, “Single Image Dehazing by Multi-Scale Fusion,” IEEE Trans. on Image Process., Vol.22, No.8, pp. 3271-3282, 2013.
  6. [6] Q. Zhu, J. Mai, and L. Shao, “A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior,” IEEE Trans. on Image Process., Vol.24, No.11, pp. 3522-3533, 2015.
  7. [7] J. Zhang, Y. Cao, and Z. Wang, “Nighttime haze removal based on a new imaging model,” Proc. of IEEE Conf. on Image Process., pp. 4557-4561, 2014.
  8. [8] J. Zhang, Y. Cao, and C. W. Chen, “Fast Haze Removal for Nighttime Image Using Maximum Reflectance Prior,” Proc. of IEEE Conf. on Comput. Vision and Pattern Recog., pp. 7016-7024, 2017.
  9. [9] A. Yamashita, S. Kato, and T. Kaneko, “Robust sensing against bubble noises in aquatic environments with a stereo vision system,” Proc. of Int. Conf. Robot. Autom., pp. 928-933, 2006.
  10. [10] F. Farhadifard, M. Radolko, and U. F. Von Lukas, “Single Image Marine Snow Removal based on a Supervised Median Filtering Scheme,” Visigrapp, Vol.4, pp. 280-287, 2017.
  11. [11] A. Yamashita, M. Fujii, and T. Kaneko, “Color registration of underwater image for underwater sensing with consideration of light attenuation,” Proc. of Int. Conf. Robot. Autom., pp. 4570-4575, 2007.
  12. [12] K. Iqbal, R. Abdul Salam, A. Osman, and A. Zawawi Talib, “Underwater image enhancement using an integrated color model,” Int. J. Comput. Sci., Vol.34, No.2, pp. 2-12, 2007.
  13. [13] H. Singh, C. Roman, O. Pizarro, R. Eustice, and A. Can, “Towards high-resolution imaging from underwater vehicles,” Int. J. Robotics Res., Vol.26, No.1, pp. 55-74, 2007.
  14. [14] M. Johnson-Roberson, M. Bryson, A. Friedman, O. Pizarro, G. Troni, P. Ozog, and J. C. Henderson, “High-resolution underwater robotic vision-based mapping and three-dimensional reconstruction for archaeology,” J. Field Robot., Vol.34, No.4, pp. 625-643, 2017.
  15. [15] G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst., Vol.310, No.1, pp. 1-26, 1980.
  16. [16] P. Drews-Jr, E. do Nascimento, F. Moraes, S. Botelho, and M. Campos, “Transmission estimation in underwater single images,” Proc. of IEEE Int. Conf. Comput. Vis., pp. 825-830, 2013.
  17. [17] J. Y. Chiang and Y.-C. Chen, “Underwater image enhancement by wavelength compensation and dehazing,” IEEE Trans. on Image Process., Vol.21, No.4, pp. 1756-1769, 2012.
  18. [18] C. Ancuti, C. O. Ancuti, T. Haber, and P. Bekaert, “Enhancing underwater images and videos by fusion,” Proc. of IEEE Conf. on Comput. Vision and Pattern Recog., pp. 81-88, 2012.
  19. [19] R. Fattal, “Single image dehazing,” Proc. SIGGRAPH ’08, pp. 1-9, 2008.
  20. [20] S. G. Narasimhan and S. K. Nayar, “Chromatic framework for vision in bad weather,” Proc. of IEEE Conf. on Comput. Vision and Pattern Recog., pp. 598-605, 2000.
  21. [21] Y. Cho and A. Kim, “Visibility enhancement for underwater visual SLAM based on underwater light scattering model,” Proc. IEEE Int. Conf. Robot. Autom., pp. 710-717, 2017.
  22. [22] K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Trans. on Pattern Analysis and Machine Intell., Vol.35, No.6, pp. 1397-1409, 2013.
  23. [23] J. Shi and C. Tomasi, “Good features to track,” Proc. of IEEE Conf. on Comput. Vision and Pattern Recog., pp. 593-600, 1994.
  24. [24] Z. Kalal, K. Mikolajczyk, and J. Matas, “Forward-Backward Error: Automatic Detection of Tracking Failures,” Int. Conf. Pattern Recog., pp. 2756-2759, 2010.
  25. [25] J. Bossu, N. Hautière, and J.-P. Tarel, “Rain or Snow Detection in Image Sequences Through Use of a Histogram of Orientation of Streaks,” Int. J. Comput. Vis., Vol.93, No.3, pp. 348-267, 2011.
  26. [26] N. Carlevaris-Bianco, A. Mohan, and R. M. Eustice, “Initial results in underwater single image dehazing,” Proc. of IEEE OCEANS, pp. 1-8, 2010.
  27. [27] A. Galdran, D. Pardo, A. Picon, and A. Alvarez-Gila, “Automatic red-channel underwater image restoration,” J. Vis. Commun. Image R., Vol.26, pp. 132-135, 2015.
  28. [28] M. Yang and A. Sowmya, “An underwater color image quality evaluation metric,” IEEE Trans. on Image Process., Vol.24, No.12, pp. 6062-6071, 2015.
  29. [29] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (SURF),” Comput. Vis. Image Und., Vol.110, No.3, pp. 346-359, 2008.
  30. [30] J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba, “Sun database: Large-scale scene recognition from abbey to zoo,” Proc. of IEEE Conf. on Comput. Vision and Pattern Recog., pp. 3485-3492, 2010.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Nov. 20, 2018