Skip to main content
SpringerLink
Log in

Efficient underwater image and video enhancement based on Retinex

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

The Retinex models the human visual system to perceive natural colors, which could improve the contrast and sharpness of the degraded image and also provide color constancy and dynamic range simultaneously. This endows the Retinex exceeding advantages for enhancing the underwater image. Based on the multi-scale Retinex, an efficient enhancement method for underwater image and video is presented in this paper. Firstly, the image is pre-corrected to equalize the pixel distribution and reduce the dominating color. Then, the classical multi-scale Retinex with intensity channel is applied to the pre-corrected images for further improving the contrast and the color. In addition, multi-down-sampling and infinite impulse response Gaussian filtering are adopted to increase processing speed. Subsequently, the image is restored from logarithmic domain and the illumination of the restored image is compensated based on statistical properties. Finally, the color is selectively preserved by the inverted gray world method depending on imaging conditions and application requirements. Five kinds of typical underwater images with green, blue, turbid, dark and colorful backgrounds and two underwater videos are enhanced and evaluated on Jetson TX2, respectively, to verify the effectiveness of the proposed method.

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

Similar content being viewed by others

References

  1. Sahu, P., Gupta, N., Sharma, N.: A survey on underwater image enhancement techniques. Int. J. Comput. Appl. 87(13), 19–23 (2014)

    Google Scholar 

  2. Tang, C., Wang, Y., Wang, S., Wang, R., Tan, M.: Floating autonomous manipulation of the underwater biomimetic vehicle-manipulator system: methodology and verification. IEEE Trans. Ind. Electron. 65(6), 4861–4870 (2018)

    Article  Google Scholar 

  3. Zhang, S., Wang, T., Dong, J., Yu, H.: Underwater image enhancement via extended multi-scale retinex. Neurocomputing 245, 1–9 (2017)

    Article  Google Scholar 

  4. Bazeille, S., Quidu, I., Jaulin, L., Malkasse, J.P.: Automatic underwater image pre-processing. In: Proceedings of the Caracterisation du Milieu Marin (2006)

  5. Iqbal, K., Salam, R.A., Osman, A., Talib, A.Z.: Underwater image enhancement using an integrated colour model. Int. J. Comput. Sci. 34, 529–534 (2007)

    Google Scholar 

  6. Yang, H.Y., Chen, P.Y., Huang, C.C., Zhuang, Y.Z., Shiau, Y.H.: Low complexity underwater image enhancement based on dark channel prior. In: Proceedings of Second International Conference on Innovations in Bio-inspired Computing and Applications, pp. 17–20. IEEE, Piscataway (2011)

  7. He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–53 (2011)

    Article  Google Scholar 

  8. Chiang, J.Y., Chen, Y.C.: Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans. Image Process. 21(4), 1756–69 (2012)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  10. Chambah, M., Semani, D., Renouf, A., Courtellemont, P., Rizzi, A.: Underwater color constancy: enhancement of automatic live fish recognition. In: Color Imaging IX: Processing, Hardcopy, and Applications, vol. 5293, pp. 157–168. International Society for Optics and Photonics, Bellingham (2004)

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

    Article  Google Scholar 

  12. Land, E.H.: The retinex. Am. Sci. 52(2), 247–264 (1964)

    Google Scholar 

  13. Joshi, K.R., Kamathe, R.S.: Quantification of retinex in enhancement of weather degraded images. In: Proceedings IEEE International Conference on Audio, Language and Image Processing, pp. 1229–1233. IEEE, Piscataway (2008)

  14. Jobson, D.J., Rahman, Z., Woodell, G.A.: A multi-scale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process.: Spec. Issue Color Process. 6, 965–976 (1997)

    Article  Google Scholar 

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

  16. Jobson, D.J., Rahman, Z., Woodell, G.A.: Properties and performance of a center/surround retinex. IEEE Trans. Image Process. 6(3), 451–62 (1997)

    Article  Google Scholar 

  17. Petro, A.B., Sbert, C., Morel, J.M.: Multiscale retinex. Image Process. Line 4, 71–88 (2014)

    Article  Google Scholar 

  18. Vahl, M.: Subseavideo (2017). https://www.igd.fraunhofer.de/en/projects/subseavideo. Accessed 2017

  19. Young, I.T., Van Vliet, L.J.: Recursive implementation of the Gaussian filter. Signal Process. 44(2), 139–151 (1995)

    Article  Google Scholar 

  20. Qiao, X., Bao, J., Zhang, H., Zeng, L., Li, D.: Underwater image quality enhancement of sea cucumbers based on improved histogram equalization and wavelet transform. Inf. Process. Agric. 4(3), 206–213 (2017)

    Google Scholar 

  21. Matkovic, K., Neumann, L., Neumann, A., Psik, T., Purgathofer, W.: Global contrast factor-a new approach to image contrast. Comput. Aesthet. 2005, 159–168 (2005)

  22. Simone, G., Pedersen, M., Hardeberg, J.Y.: Measuring perceptual contrast in digital images. J. Vis. Commun. Image Represent. 23(3), 491–506 (2012)

    Article  Google Scholar 

  23. Wang, Y., Li, N., Li, Z., Gu, Z., Zheng, H., Zheng, B., Sun, M.: An imaging-inspired no-reference underwater color image quality assessment metric. Comput. Electr. Eng. 70, 1–10 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Chong Tang, Shuo Wang, Yu Wang & Min Tan

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Chong Tang & Shuo Wang

  3. Fraunhofer Institute for Computer Graphics Research, 18059, Rostock, Germany

    Uwe Freiherr von Lukas & Matthias Vahl

  4. University of Rostock, 18051, Rostock, Germany

    Uwe Freiherr von Lukas

  5. CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, 200031, China

    Shuo Wang

Authors
  1. Chong Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Uwe Freiherr von Lukas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthias Vahl
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Min Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuo Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported in part by the National Natural Science Foundation of China under Grant 61703401, Grant U1713222, Grant 61773378, Grant U1806204, in part by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant 61421004, in part by Beijing Science and Technology Project under Grant Z181100003118006, in part by Youth Innovation Promotion Association CAS, in part by the Early Career Development Award of SKLMCCS and by the UCAS (UCAS[2015]37) Joint PhD Training Program.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, C., von Lukas, U.F., Vahl, M. et al. Efficient underwater image and video enhancement based on Retinex. SIViP 13, 1011–1018 (2019). https://doi.org/10.1007/s11760-019-01439-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01439-y

Keywords

Search

Navigation