Skip to main content
SpringerLink
Log in

RETRACTED ARTICLE: Learning-based approach to underwater image dehazing using CycleGAN

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

This article was retracted on 30 November 2023

An Editorial Expression of Concern to this article was published on 28 September 2021

This article has been updated

Abstract

In this paper, we have proposed a novel deep learning-based underwater image dehazing method. The images taken under water are affected by light transportation properties like refraction, reflection, scattering, and absorption. Deep learning techniques give great results in the removal of haze in underwater degraded images. But the major challenge we are facing is the need of many images from the same location to train the model. The proposed system addressed this challenge by using CycleGAN, capable of producing synthetic underwater images from RGB in-air images, and is validated using images taken from different locations and with different characteristics. Qualitative evaluation has been done based upon visual analysis. Quantitative analysis is based upon the calculation of various parameters such as MSE, RMSE, and Euclidean distance. In addition, SSIM Loss has also been evaluated and compared. The model is tested for underwater images taken under various circumstances, and the quantitative and qualitative results obtained for these images are quite satisfactory.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Change history

References

  • Ananthi M, Vijayakumar K (2021) Stock market analysis using candlestick regression and market trend prediction (CKRM). J Ambient Intell Humaniz Comput 12. https://doi.org/10.1007/s12652-020-01892-5

  • Anathi M, Vijayakumar K (2020) An intelligent approach for dynamic network traffic restriction using MAC address verification. Comput Commun 154:559–564. https://doi.org/10.1016/j.comcom.2020.02.021

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

  • Anwar S, Li C, Porikli F (2018) Deep underwater image enhancement,ArXiv180703528 Cs

  • ArnoldBos A, Malkasse JP, Kervern G (2005) A preprocessing framework for automatic underwater images denoising, in Proc. Eur. Conf. Propag. Syst., Brest, France, Mar., pp. 15_18

  • Berman D, Levy D, Avidan S, Treibitz T (2020) Underwater single image color restoration using haze-lines and a new quantitative dataset. IEEE transactions on pattern analysis and machine intelligence. https://doi.org/10.1109/tpami.2020.2977624

  • Chen Y-S, Wang Y-C, Kao M-H, Chuang Y-Y (2018) Deep photo enhancer: unpaired learning for image enhancement from photographs with GANs. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pages 6306– 6314.

  • Drews P, Nascimento E, Botelho S, Campos M (2016) Underwater depth estimation and image restoration based on single images. IEEE Comput Graph Appl 36(2):24–35

    Article  Google Scholar 

  • Fabbri C, Islam MJ, Sattar J (2018) Enhancing underwater imagery using generative adversarial networks. pp 7159–7165. https://doi.org/10.1109/ICRA.2018.8460552

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

    Article  Google Scholar 

  • Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville (2017a) Improved training of wasserstein gans,arXiv preprint arXiv:1704.0002

  • Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville (2017b) Improved training of wasserstein gans, arXiv preprint arXiv:1704.00028

  • Ignatov, N. Kobyshev, R. Timofte, K. Vanhoey, and L. Van Gool (2017) DSLR-quality photos on mobile devices with deep convolutional networks. In IEEE International Conference on Computer Vision (ICCV), pages 3277–3285

  • Iqbal RAK, Salam A, Osman, Talib AZ (2007) Underwater image enhancement using an integrated colour model. Int. J Comput Sci 34(2):1–6

    Google Scholar 

  • Isola P, Zhu J-Y, Zhou T, Efros AA (2017) Image-to- image translation with conditional adversarial networks. pp 1125–1134. https://doi.org/10.1109/CVPR.2017.632

  • Li C, Ji C, Cong R, Pang Y, Wang B (2016) Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior. IEEE Trans Image Process 25(12):5664–5677

    Article  Google Scholar 

  • Li J, Skinner KA, Eustice RM, Johnson-Roberson M (2018) WaterGAN: unsupervised generative network to enable real-time color correction of monocular underwater images. IEEERobot Autom Lett 3(1):387–394

    Google Scholar 

  • Nithya M, Vijayakumar K (2021) Secured segmentation for ICD datasets. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02009-8

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

    Article  Google Scholar 

  • Pérez J, Attanasio A, Nechyporenko N, Sanzi P (2017) A deep learning approach for underwater image enhancement.183–192. https://doi.org/10.1007/978-3-319-59773-7_19

  • Wang Y, Zhang J, Cao Y, Wang Z (2017) A deep CNN method for underwater image enhancement. pp 1382–1386. https://doi.org/10.1109/ICIP.2017.8296508

  • Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, MVJ College of Engineering, Bangalore, India

    Shima Ramesh Maniyath

  2. Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, OMR, Chennai, India

    K Vijayakumar

  3. Department of Electronics and Communication Engineering, Noida Institute of Engineering and Technology, Greater Noida, UP, India

    Laxman Singh

  4. Department of Computer Science, Institute of Information Technology and Management, New Delhi, India

    Sudhir Kumar Sharma

  5. Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria

    Tunde Olabiyisi

Authors
  1. Shima Ramesh Maniyath
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K Vijayakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laxman Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sudhir Kumar Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tunde Olabiyisi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K Vijayakumar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interests.

Additional information

Responsible Editor: Sheldon Williamson

This article is part of the Topical Collection on Environment and Low Carbon Transportation

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12517-023-11815-1

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.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maniyath, S.R., Vijayakumar, K., Singh, L. et al. RETRACTED ARTICLE: Learning-based approach to underwater image dehazing using CycleGAN. Arab J Geosci 14, 1908 (2021). https://doi.org/10.1007/s12517-021-07742-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-021-07742-8

Keywords

Search

Navigation