Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Adaptive underwater image enhancement based on color compensation and fusion

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

Abstract

Underwater images typically suffer from color deviation, low contrast and poor visibility. To deal with these problems, we propose an adaptive underwater image enhancement method based on color compensation and fusion. We first apply adaptive color compensation to the original underwater image, followed by color correction. And we continue to apply local adaptive contrast enhancement to the color-corrected image. Finally, we combine the contrast-enhanced image with the color-corrected image using a multi-scale fusion technique. The algorithm can more successfully increase underwater image deviation and blurring, as well as image contrast and local detail enhancement, according to experiments, which demonstrate that it is more adaptable to varied underwater scene types than existing methods.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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. Zhou, Y., Yan, K., Li, X.: Underwater image enhancement via physical-feedback adversarial transfer learning. IEEE J. Oceanic Eng. 47(1), 76–87 (2022)

    Article  Google Scholar 

  2. Zhang, W., Dong, L., Xu, W., et al.: Retinex-inspired color correction and detail preserved fusion for under image enhancement. Comput. Electron. Agric. 192, 1006585 (2021)

    Google Scholar 

  3. Zhou, J., Zhang, D., et al.: Auto color correction of underwater images Utilizing depth information. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022)

    Google Scholar 

  4. Mazel, C.H.: In situ measurement of reflectance and fluorescence spectra to support hyperspectral remote sensing and marine biology research. In: Proc. IEEE OCEANS Conf., pp.1–4 (2006)

  5. Zhang, W., Dong, L., et al.: Enhancing underwater image via color correction and Bi-interval contrast enhancement. Signal Process. Image Commun. 90, 116030 (2021)

    Article  Google Scholar 

  6. Zhou, J., Yang, T., et al.: Underwater image processing technologies: a comprehensive review, challenges, and recent trends. Appl. Intell. 54, 1–28 (2022)

    Google Scholar 

  7. Zhou, J., Yang, T., et al.: Underwater image restoration via backscatter pixel prior and color compensation. Eng. Appl. Artif. Intell. 111, 104785 (2022)

    Article  Google Scholar 

  8. He, K., et al.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. 33(12), 2341–2353 (2011)

    Article  Google Scholar 

  9. Zhang, W., Liu, W., et al.: Underwater single-image restoration with transmission estimation using color constancy. J. Mar. Sci. Eng. 10(3), 430 (2022)

    Article  Google Scholar 

  10. Dai, C., Lin, M., et al.: Single underwater image restoration by decomposing curves of attenuating color. Opt. Laser Technol. 123, 105947 (2020)

    Article  Google Scholar 

  11. Ancuti, C., Ancuti, C.O., et al.: Enhancing underwater images and videos by fusion. In: IEEE Conf. Comput. Vis. Pattern Recognit, pp. 81–88 (2012)

  12. Zhang, C., et al.: Underwater image enhancement based on differential channel gain and improved retinex. Laser Optoelectron. Prog. 58(14), 156–163 (2021)

    Google Scholar 

  13. Yu, H., Li, X., et al.: Underwater image enhancement based on color-line model and homomorphic filtering. Signal Image Video Process. 16, 83–91 (2022)

    Article  Google Scholar 

  14. Ancut, C.O., Ancuti, C., et al.: Color balance and fusion for underwater image enhancement. IEEE Trans. Image Process. 27(1), 379–393 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  15. Yang, A., et al.: Underwater image restoration based on color cast removal and dark channel prior. J. Electr. Eng. Technol. 37(11), 2541–2547 (2015)

    Google Scholar 

  16. Li, C., Guo, J., et al.: A hybrid method for underwater image correction. Pattern Recognit. Lett. 94, 62–67 (2017)

    Article  Google Scholar 

  17. Yang, M., et al.: An underwater image comprehensive enhancement algorithm based on color compensation. J. Graph. 42(1), 59–64 (2021)

    Google Scholar 

  18. Dai, C., et al.: Color compensation based on bright channel and fusion for underwater image enhancement. Acta Opt. Sin. 38(11), 86–95 (2018)

    Google Scholar 

  19. Zhang, W., Zhuang, P., et al.: Underwater image enhancement via minimal color loss and locally adaptive contrast enhancement. IEEE Trans. Image Process. 31, 3997–4010 (2022)

    Article  Google Scholar 

  20. Ancuti, C.O., et al.: Enhancing by saliency-guided decolorization. In: IEEE Conf. Comput. Vis. Pattern Recognit, pp. 257–264 (2011)

  21. Drews, P. Jr., do Nascimento, E., et al.: Transmission estimation in underwater single images. In: IEEE Int. Conf. Comput. Vis, pp. 825–830 (2013)

  22. Peng, Y.-T., Cosman, P.C.: Underwater image restoration based on image blurriness and light absorption. IEEE Trans. Image Process. 26(4), 1579–1594 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  23. Song, W., Wang, Y., et al.: A rapid scene depth estimation model based on underwater light attenuation prior for underwater image restoration. In: Adv. Multi Inf. Process, pp. 678–688 (2018)

  24. Huang, D., Wang, Y., et al.: Shallow-water image enhancement using relative global histogram stretching based on adaptive parameter acquisition. In: Int. Cof. Multimed Model, pp. 453–465 (2018)

  25. Zhang, W., Wang, Y., Li, C.: Underwater image enhancement by attenuated color channel correction and detail preserved contrast enhancement. IEEE J. Ocean. Eng. 47(3), 718–735 (2022)

    Article  Google Scholar 

  26. Li, C., et al.: An underwater image enhancement benchmark dataset and beyond. IEEE Trans. Image Process. 29, 4376–4389 (2018)

    Article  MATH  Google Scholar 

Download references

Funding

This research was supported by the Natural Science Foundation of Shandong Province of China (No. ZR2020ME267) and Shandong Province Key R&D Program (Major Science and Technology Innovation Project) (No. 2019JZZY020703).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Shandong University, Jinan, 250061, Shandong, China

    Xuedong Zhu, Mingxing Lin, Mingyue Zhao & Wenjing Fan

  2. School of Mechanical Engineering, National Demonstration Center for Experimental Mechanical Engineering, Shandong University, Jinan, 250061, Shandong, China

    Xuedong Zhu, Mingxing Lin, Mingyue Zhao & Wenjing Fan

  3. Key School of Mechanical Engineering, Laboratory of High-Efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan, 250061, Shandong, China

    Xuedong Zhu, Mingxing Lin, Mingyue Zhao & Wenjing Fan

  4. School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao, 266520, Shandong, China

    Chenggang Dai

Authors
  1. Xuedong Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingxing Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingyue Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wenjing Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chenggang Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mingxing Lin.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, X., Lin, M., Zhao, M. et al. Adaptive underwater image enhancement based on color compensation and fusion. SIViP 17, 2201–2210 (2023). https://doi.org/10.1007/s11760-022-02435-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-022-02435-5

Keywords

Search

Navigation