Skip to main content
SpringerLink
Log in

Underwater Image Enhancement by the Combination of Dehazing and Color Correction

  • Conference paper
  • First Online:
Advances in Multimedia Information Processing – PCM 2018 (PCM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11166))

Included in the following conference series:

Abstract

Underwater image processing is crucial for many practical applications in the ocean filed, which is not a trivial thing since the environment of underwater is often complicated and short of light. The major difficulty is that a captured image is fuzzy, under-exposed and often has the color cast due to the fact that the light is absorbed and scattered. To overcome those difficulties, we propose a new underwater image enhancement method, which is composed of two successive vital processings: the dehazing and color correction. Firstly, considering the characteristic of light propagation under water, we propose a new dehazing algorithm to restore the visibility of degraded underwater images based on the dark channel prior, through building up the relationship of the transmission rates among three color channels. Then, to further improve the image quality, we adopt an effective color correction method on the obtained haze-free underwater images. We conduct extensive experiments under measure tests of both subjective and objective, and the results show that our method is superior to several existing approaches.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://sites.google.com/site/kyutech8luhuimin/underwater_image_datasets.

References

  1. Spectral dependence of the scattering coefficient in case: 1 and case 2 waters. Appl. Opt. 38(12), 2377–2383 (1999). https://doi.org/10.1364/ao.38.002377. http://www.ncbi.nlm.nih.gov/pubmed/18319803

    Article  Google Scholar 

  2. Ancuti, C., Ancuti, C.O., Haber, T., Bekaert, P.: Enhancing underwater images and videos by fusion. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 81–88 (2012). https://doi.org/10.1109/CVPR.2012.6247661

  3. Carlevaris-Bianco, N., Mohan, A., Eustice, R.M.: Initial results in underwater single image dehazing. In: Oceans 2010, pp. 1–8. IEEE (2010)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  5. Duntley, S.Q.: Light in the sea. J. Opt. Soci. Am. 53(2), 214–233 (1963)

    Article  Google Scholar 

  6. Fattal, R.: Single image dehazing. ACM Trans. Graph. 27(3), 1 (2008). https://doi.org/10.1145/1360612.1360671

    Article  Google Scholar 

  7. Fattal, R.: Dehazing using color-lines. ACM Trans. Graph. (TOG) 34(1), 13 (2014)

    Article  Google Scholar 

  8. Fu, X., Liao, Y., Zeng, D., Huang, Y., Zhang, X.P., Ding, X.: A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation. IEEE Trans. Image Process. 24(12), 4965 (2015). A Publication of the IEEE Signal Processing Society

    Article  MathSciNet  Google Scholar 

  9. Fu, X., Zhuang, P., Huang, Y., Liao, Y., Zhang, X.P., Ding, X.: A retinex-based enhancing approach for single underwater image. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 4572–4576. IEEE (2014)

    Google Scholar 

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

    Article  Google Scholar 

  11. Getreuer, P.: Automatic color enhancement (ACE) and its fast implementation. Image Process. Line 2, 266–277 (2012)

    Article  Google Scholar 

  12. Gordon, H.R.: Can the lambert-beer law be applied to the diffuse attenuation coefficient of ocean water? Limnol. Oceanogr. 34(8), 1389–1409 (1989)

    Article  Google Scholar 

  13. He, K., Sun, J., Tang, X.: Guided image filtering. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 1–14. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15549-9_1

    Chapter  Google Scholar 

  14. Jaspers, C.A.M.: Histogram equalization (2004)

    Google Scholar 

  15. He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010)

    Google Scholar 

  16. Kim, J.H., Jang, W.D., Sim, J.Y., Kim, C.S.: Optimized contrast enhancement for real-time image and video dehazing. J. Vis. Commun. Image Represent. 24(3), 410–425 (2013)

    Article  Google Scholar 

  17. Li, C.Y., Guo, J.C., Cong, R.M., Pang, Y.W., Wang, B.: Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior. IEEE Trans. Image Process. 25(12), 5664–5677 (2016). https://doi.org/10.1109/TIP.2016.2612882

    Article  MathSciNet  Google Scholar 

  18. Lu, H., Li, Y., Zhang, L., Serikawa, S.: Contrast enhancement for images in turbid water. J. Opt. Soci. Am. A Opt. Image Sci. Vis. 32(5), 886 (2015)

    Article  Google Scholar 

  19. Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2013)

    Article  Google Scholar 

  20. Panetta, K., Gao, C., Agaian, S.: Human-visual-system-inspired underwater image quality measures. IEEE J. Ocean. Eng. 41(3), 541–551 (2016). https://doi.org/10.1109/JOE.2015.2469915

    Article  Google Scholar 

  21. Torres-Méndez, L.A., Dudek, G.: Color correction of underwater images for aquatic robot inspection. In: Rangarajan, A., Vemuri, B., Yuille, A.L. (eds.) EMMCVPR 2005. LNCS, vol. 3757, pp. 60–73. Springer, Heidelberg (2005). https://doi.org/10.1007/11585978_5

    Chapter  Google Scholar 

  22. Wen, H.: Single underwater image enhancement with a new optical model. In: ISCAS, pp. 753–756 (2013)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the Project of National Engineering Laboratory for Video Technology -Shenzhen Division, Shenzhen Key Laboratory for Intelligent Multimedia and Virtual Reality under Grant ZDSYS201703031405467, and the Shenzhen Municipal Development and Reform Commission (Disciplinary Development Program for Data Science and Intelligent Computing) under Grant 1230233753.

Author information

Authors and Affiliations

  1. Peking University Shenzhen Graduate School, Lishui Road 2199, Nanshan District, Shenzhen, 518055, Guangdong Province, China

    Wenhao Zhang, Ge Li & Zhenqiang Ying

Authors
  1. Wenhao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ge Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhenqiang Ying
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ge Li .

Editor information

Editors and Affiliations

  1. Hefei University of Technology, Hefei, China

    Richang Hong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. University of Tokyo, Tokyo, Japan

    Toshihiko Yamasaki

  4. Hefei University of Technology, Hefei, China

    Meng Wang

  5. City University of Hong Kong, Hong Kong, Hong Kong

    Chong-Wah Ngo

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, W., Li, G., Ying, Z. (2018). Underwater Image Enhancement by the Combination of Dehazing and Color Correction. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11166. Springer, Cham. https://doi.org/10.1007/978-3-030-00764-5_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00764-5_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00763-8

  • Online ISBN: 978-3-030-00764-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation