Skip to main content
SpringerLink
Log in

Non-uniform de-Scattering and de-Blurring of Underwater Images

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

Abstract

Optical underwater images often demonstrate low contrast, heavy scatter, and color distortion. Contrast enhancement methods have been proposed to solve these issues. However, such methods typically do not consider high-level inhomogeneous scatter removal and do not focus on real-scene color restoration. We proposed a hierarchical transmission fusion method and a color-line ambient light estimation method for image de-scattering from a single input image. Our proposed method can be summarized into three steps. Firstly, we take the dark channel as prior information to estimating the preliminary transmission and ambient light. In the second step, we then use color lines to estimate the refined ambient light in selected patches. The refined transmission is obtained by hierarchical transmission maps using maximum local energy-based fusion at different turbidity levels. We then use a joint normalized filter to obtain the final transmission. Finally, a chromatic color correction method and de-blurring algorithm are used to recover the scene color. Experimental results demonstrate that the accurate estimation of the depth map and ambient light by the proposed method can recover visually appealing images with sharp details.

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
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Schechner Y, Karpel N (2005) Recovery of underwater visibility and structure by polarization analysis. IEEE J Ocean Eng 30:570–587

    Article  Google Scholar 

  2. 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 43:995–1009

    Article  Google Scholar 

  3. Roser M, Dunbabin M, and Geiger A (2014) “Simultaneous underwater visibility assessment, enhancement and improved stereo,” In Proc. of IEEE ICRA, pp. 3840–3847

  4. Treibitz T, Murez Z, Mitchell B, and Kriegman D (2012) “Shape from fluorescence,” In Proc. of ECCV, pp. 292–306

  5. Ouyang B, Dalgleish F, Caimi F, Vuorenkoski A, Giddings T, Shirron J (2012) Image enhancement for underwater pulsed laser line scan imaging system. Proc SPIE 8372:837201–837208

    Article  Google Scholar 

  6. Lee Y, Gibson K, Lee Z, and Nguyen T (2014) “Stereo image defogging,” In Proc. of IEEE ICIP, pp. 5427–5431

  7. 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

  8. Zuiderveld K (1994) “Contrast limited adaptive histogram equalization,” Graphics Gems IV, Academic Press

  9. Fattal R (2014) Dehazing using color-lines. ACM Trans Graph 27:1–8

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Gasparini F, Corchs S, Schettini R (2007) Low quality image enhancement using visual attention. Opt Eng 46(4):0405021–0405023

    Article  Google Scholar 

  12. Lu H, Li Y, Nakashima S, Serikawa S (2015) “Single image dehazing through robust atmospheric light estimation,”. Multimed Tools Appl, pp. 1–16

  13. Lu H, Li Y, Zhang L, Serikawa S (2015) Contrast enhancement for image in turbid water. J Opt Soc Am A 32(5):886–893

    Article  Google Scholar 

  14. Bazeille S, Quidu I, Jaulin L, Malkasse JP (2006) “Automatic underwater image pre-processing,” In Proc. of Caracterisation Du Milieu Marin, Brest, France, pp. 1–8

  15. Nicholas CB, Anush M, Eustice RM (2010) “Initial results in underwater single image dehazing,” In Proc. of IEEE Oceans2010, Seattle, USA, pp. 1–8

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

    Article  MathSciNet  MATH  Google Scholar 

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

  18. 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 

  19. Bonin F, Burguera A, Oliver G (2011) Imaging system for advanced underwater vehicles. J Marit Res 8(1):65–86

    Google Scholar 

  20. Emberton S, Chittka L, Cavallaro A (2015) “Hierarchical rank-based veiling light estimation for underwater dehazing,” In Proc. of British Machine Vision Conference, pp. 1–12

  21. Codevilla F, Gaya J, Filho N, Botelho S (2015) “Achieving turbidity robustness on underwater images local feature detection,” In Proc. of British Machine Vision Conference, pp. 1–13

  22. 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

    Google Scholar 

  23. Lu H, Zhang L, Serikawa S (2012) Maximum local energy: an effective approach for multisensor image fusion in beyond wavelet transform domain. Comput Math Appl 64(5):996–1003

    Article  MATH  Google Scholar 

  24. Qu X, Yan J, Xie G, Zhu Z (2007) A novel image fusion algorithm based on bandelet transform. Chin Opt Lett 5(10):569–572

    Google Scholar 

  25. Coleman T, Li Y (1996) An interior, trust region approach for nonlinear minimization subject to bounds. SIAM J Optim 6:418–445

    Article  MathSciNet  MATH  Google Scholar 

  26. R. Wang, and D. Tao, “Recent process in image deblurring,” arxiv:1409.6838, pp. 1–53, 2014

  27. Dong W, Zhang L, Shi G, Li X (2013) Nonlocally centralized sparse representation for image restoration. IEEE Trans Image Process 22(4):1620–1630

    Article  MathSciNet  MATH  Google Scholar 

  28. Chen S, Donoho D, Saunders M (2001) Atomic decompositions by basis pursuit. SIAM Rev 43:129–159

    Article  MathSciNet  MATH  Google Scholar 

  29. Narasimhan S, Gupta M, Donner C, Ramamoorthi R, Nayar S, Jensen H (2006) Acquiring scattering properties of participating media by dilution. ACM Trans Graph 25(3):1003–1012

    Article  Google Scholar 

  30. Murez Z, Treibitz T, Romamoorthi R, Kriegman D (2015) Photometric stereo in a scattering medium. ICCV 2015:1–10

    Google Scholar 

  31. Hautiere N, Tarel J, Aubert D (2008) “Towards fog-free in-vehicle vision systems through contrast restoration,” in Proc. of CVPR, pp. 1–8

  32. Li Y, Tan R, Brown MS (2015) Nighttime haze removal with glow and multiple light colors. Proc of ICCV 2015:1–8

    Google Scholar 

  33. Zhu Q, Mai J, Shao L (2015) A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process 24(11):3522–3533

    Article  MathSciNet  Google Scholar 

  34. Lu H, Li Y, Xu X, He L, Li Y, Dansereau D, Serikawa S (2016) Underwater image descattering and quality assement. Proc of ICIP 2016:1998–2002

    Google Scholar 

  35. Li Y, Lu H, Serikawa S (2013) Segmentation of offshore oil spill images in leakage acoustic detection system. Res. J. Chem. Environ 17:36–41

    Google Scholar 

  36. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  37. Szegedy C et al (2015) Going deeper with convolutions. Proc of CVPR 2015:1–8

    Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Leading Initiative for Excellent Young Researcher (LEADER) of Ministry of Education, Culture, Sports, Science and Technology-Japan (16809746), Grants-in-Aid for Scientific Research of JSPS (17 K14694), Research Fund of Chinese Academy of Sciences (No.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 (1510), Research Fund of The Telecommunications Advancement Foundation, and Fundamental Research Developing Association for Shipbuilding and Offshore.

Author information

Authors and Affiliations

  1. Yangzhou University, Yangzhou, China

    Yujie Li, Hyoungseop Kim & Seiichi Serikawa

  2. Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu, Hyoungseop Kim & Seiichi Serikawa

  3. Providence University, Taichung City, Taiwan

    Kuan-Ching Li

Authors
  1. Yujie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huimin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kuan-Ching Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hyoungseop Kim
    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

Corresponding author

Correspondence to Huimin Lu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Lu, H., Li, KC. et al. Non-uniform de-Scattering and de-Blurring of Underwater Images. Mobile Netw Appl 23, 352–362 (2018). https://doi.org/10.1007/s11036-017-0933-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-017-0933-7

Keywords

Search

Navigation