Skip to main content
SpringerLink
Log in

Underwater Optical Image Processing: a Comprehensive Review

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Underwater cameras are widely used to observe the sea floor. They are usually included in autonomous underwater vehicles (AUVs), unmanned underwater vehicles (UUVs), and in situ ocean sensor networks. Despite being an important sensor for monitoring underwater scenes, there exist many issues with recent underwater camera sensors. Because of light’s transportation characteristics in water and the biological activity at the sea floor, the acquired underwater images often suffer from scatters and large amounts of noise. Over the last five years, many methods have been proposed to overcome traditional underwater imaging problems. This paper aims to review the state-of-the-art techniques in underwater image processing by highlighting the contributions and challenges presented in over 40 papers. We present an overview of various underwater image-processing approaches, such as underwater image de-scattering, underwater image color restoration, and underwater image quality assessments. Finally, we summarize the future trends and challenges in designing and processing underwater imaging sensors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. McGlamery B (1979) A computer model for underwater camera system. Proc SPIE 208:221–231

    Article  Google Scholar 

  2. Lu H, Li Y, Serikawa S (2016) Computer vision for ocean observing. Artificial Intelligence and Computer Vision:1–16

  3. Bonin F, Burgeara A, Oliver G (2011) Imaging system for advanced underwater vehicles. Journal of Maritime Research 8(1):65–86

    Google Scholar 

  4. Lu H, Li Y, Xu X, Li J, Liu Z, Li X, Yang J, Serikawa S (2016) Underwater image enhancement method using weighted guided trigonometric filtering and artificial light correction. Journal of Vision Communication and Image Representation 38:504–516

    Article  Google Scholar 

  5. Yemelyanov K, Lin S, Pugh E, Engheta N (2006) Adaptive algorithms for two-channel polarization sensing under various polarization statistics with nonuniform distributions. Appl Opt 45(22):5504–5520

    Article  Google Scholar 

  6. Schechner Y, Averbuch Y (2007) Regularized image recovery in scattering media. IEEE Trans Pattern Anal Mach Intell 29(9):1655–1660

    Article  Google Scholar 

  7. Tan C, Sluzek A, Jiang T (2007) Range gated imaging system for underwater robotic vehicle. In: Proceedings of IEEE international symposium on the applications of ferroelectrics, pp 1–7

  8. Kocak D, Dalgleish F, Caimi F, Schechner Y (2008) A focus on recent developments and trends in underwater imaging. Mar Technol Soc J 42(1):52–67

    Article  Google Scholar 

  9. Tan C, Sluzek G, He D (2005) A novel application of range-gated underwater laser imaging system in near target turbid medium. Opt Lasers Eng 43(9):995–1009

    Article  Google Scholar 

  10. Li H, Wang X, Bai T, Jin W, Huang Y, Ding K (2009) Speckle noise suppression of range gated underwater imaging system. Appl Opt 38(18):3937–3944

    Google Scholar 

  11. Arnold-Bos A, Malkasse J, and Kerven G (2005) A pre-processing framework for automatic underwater images denoising. In: Proceedings of the European conference on propagation and systems, breast, France, pp 1–8

  12. Rizzi A, Gatta C, Marini D (2003) A new algorithm for unsupervised global and local color correction. Pattern Recogn Lett 24(11):1663–1677

    Article  Google Scholar 

  13. Arnold-Bos A, Malkasset J, and Kervern G (2005) Towards a model-free denoising of underwater optical images. In: Proceedings of the IEEE Europe Oceans Conference, pp 527–532

  14. Chambah M, Demani D, Renouf A, Courtellemont P, and Rizzi A (2004) Underwater color constancy: enhancement of automatic live fish recognition. In Proceedings of SPIE, pp 157–168

  15. Murez Z, Treibitz T, Ramamoorthi R, and Kriegman D (2015) Photometric stereo in a scattering medium. In: Proceedings of IEEE international conference on computer vision, pp 3415–3423

  16. Treibitz T, Schechner Y (2012) Turbid scene enhancement using multi-directional illumination fusion. IEEE Trans Image Process 21(11):4662–4667

    Article  MathSciNet  MATH  Google Scholar 

  17. Roser M, Dunbabin M, and Geiger A (2014) Simultaneous underwater visibility assessment, enhancement and improved stereo. In: Proceedings of IEEE international conference on robotics and automation, pp 1–8

  18. Fattal R (2008) Single image dehazing. ACM Trans Graph 27(3):1–8

    Article  Google Scholar 

  19. Fattal R (2014) Dehazing using color-lines. ACM Trans Graph 34(1):1–10

    Article  Google Scholar 

  20. He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353

    Article  Google Scholar 

  21. Chiang J, Chen Y (2012) Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans Image Process 21(4):1756–1769

    Article  MathSciNet  MATH  Google Scholar 

  22. Lu H, Li Y, Zhang L, Serikawa S (2015) Contrast enhancement for images in turbid water. Journal of Optical Society of America A 32(5):886–893

    Article  Google Scholar 

  23. Garcia R, Nicosevici T, and Cufi C (2002) On the way to solve lighting problems in underwater imaging. In Proc. of MTS/IEEE Oceans ‘02, pp 1018–1024

  24. Zuiderveld K (1994) Contrast limited adaptive histogram equalization. Academic Press, Graphics gems IV, pp 474–485

  25. Ancuti C, Ancuti CO, Haber T, and Bekaert P (2012) Enhancing underwater images and videos by fusion. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, pp 81–88

  26. Galdran A, Pardo D, Picon A, Gila AA (2015) Automatic red-channel underwater image restoration. J Vis Commun Image Represent 26:132–145

    Article  Google Scholar 

  27. Gibson K (2015) Preliminary results in using a joint contrast enhancement and turbulence mitigation method for underwater optical imaging. In Proc of IEEE Oceans 2015:1–5. https://searchworks.stanford.edu/view/10257123

  28. Serikawa S, Lu H (2014) Underwater image dehazing using joint trilateral filter. Comput Electr Eng 40(1):41–50

    Article  Google Scholar 

  29. Bazeille S, Quidu I, Jaulin L, and Malkasse JP (2006) Automatic underwater image pre-processing. In: Proceedings of Caracterisation Du Milieu Marin, pp 1–8

  30. Iqbal K, Salam R, Osman A, Talib A (2007) Underwater image enhancement using an integrated color model. Int J Comput Sci 34(2):1–8

    Google Scholar 

  31. Torres-Mendez L, and Dufdek G (2005) Color correction of underwater images for aquatic robot inspection. In: Proc. of 5th International workshop on energy minimization method in computer vision and pattern recognition, pp 60–73

  32. Ahlen J, Sundgren D, Bengtsson E (2007) Application of underwater hyperspectral data for color correction purposes. Pattern Recognition and Image Analysis 17(1):170–173

    Article  Google Scholar 

  33. Petie F, Capelle-Laize A, and Carre P (2009) Underwater image enhancement by attenuation inversion with quaternions. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing, pp 1177–1180

  34. Lu H, Li Y, and Serikawa S (2015) Single underwater image descattering and color correction. In: Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing, pp 1623–1627

  35. Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  36. Panetta K, Gao C, Agaian S (2015) Human visual system inspired underwater image quality measures. IEEE J Ocean Eng:1–11

  37. Lu H, and Serikawa S (2014) Underwater scene enhancement using weighted guided median filter. In: Proceedings of IEEE international conference on multimedia and expo, pp. 1–6

  38. Yang M, Sowmya A (2015) An underwater color image quality evaluation metric. IEEE Trans Image Process 24(12):6062–6071

    Article  MathSciNet  Google Scholar 

  39. Hou W, Weidemann A (2007) Objectively assessing underwater image quality for the purpose of automated restoration. Proc SPIE 6575:1–10

    Google Scholar 

  40. Arredondo M, and Lebart K (2005) A methodology for the systematic assessment of underwater video processing algorithms. In: Proceedings of IEEE Europe oceans conference, pp 362–367

  41. Lu H, Li Y, Xu X, He L, Dansereau D, and Serikawa S (2016) Underwater image descattering and quality assessment. In: Proceedings of IEEE international conference on image processing, pp. 1998–2002

  42. Li Y, Lu H, Li J, Li X, Li Y, Serikawa S (2016) Underwater image de-scattering and classification by deep neural network. Computers & Electrical Engineering 54:68–77

    Article  Google Scholar 

  43. Zhang Y (2016) GroRec: a group-centric intelligent recommender system integrating social, mobile and big data technologies. IEEE Trans Serv Comput 9(5):786–795

    Article  Google Scholar 

  44. Zhang Y, Chen M, Huang D, Wu D, Li Y (2016) iDoctor: personalized and professionalized medical recommendations based on hybrid matrix factorization. Futur Gener Comput Syst:1–15

  45. Zhang Y, Chen M (2016) Definable networking. Cloud based 5G wireless networks, pp 33–58

  46. Schettini R, Corchs S (2010) Underwater image processing: state of the art of restoration and image enhancement methods. EURASIP Journal on Advances in Signal Processing 2010:1–14

    Article  Google Scholar 

  47. Hou W (2013) Ocean sensing and monitoring. SPIE Press

Download references

Acknowledgements

This work was supported by JSPS KAKENHI (17K14694), Leading Initiative for Excellent Young Researcher (LEADER) of Ministry of Education, Culture, Sports, Science and Technology-Japan (16809746), Research Fund of Chinese Academy of Sciences (MGE2015KG02), Research Fund of State Key Laboratory of Marine Geology in Tongji University (MGK1608), Research Fund of State Key Laboratory of Ocean Engineering in Shanghai Jiaotong University (1315; 1510), and Research Fund of The Telecommunications Advancement Foundation and Fundamental Research Developing Association for Shipbuilding and Offshore.

Author information

Authors and Affiliations

  1. Kyushu Institute of Technology, 1-1 Sensui, Tobata, Kitakyushu, 804-8550, Japan

    Huimin Lu, Yujie Li, Seiichi Serikawa & Hyoungseop Kim

  2. Chinese Academy of Science, 7 Nanhai Rd., Shinan, Qingdao, 266000, China

    Huimin Lu

  3. Yangzhou University, 198 Huayang West Rd., Hanjiang, Yangzhou, 225127, China

    Yujie Li

  4. Nanjing Normal University, 122 Ninghai Rd., Gulou, Nanjing, 210097, China

    Yudong Zhang

  5. Huazhong University of Science and Technology, 1037 Luoyu Rd., Hongshan, Wuhan, 430074, China

    Min Chen

Authors
  1. Huimin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yujie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yudong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Min Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Seiichi Serikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hyoungseop Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Huimin Lu or Yujie Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, H., Li, Y., Zhang, Y. et al. Underwater Optical Image Processing: a Comprehensive Review. Mobile Netw Appl 22, 1204–1211 (2017). https://doi.org/10.1007/s11036-017-0863-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-017-0863-4

Keywords

Search

Navigation