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All issues Volume 78 (2025) ITM Web Conf., 78 (2025) 04031 References
Open Access
Issue
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
Article Number 04031
Number of page(s) 12
Section Foundations and Frontiers in Multimodal AI, Large Models, and Generative Technologies
DOI https://doi.org/10.1051/itmconf/20257804031
Published online 08 September 2025
  1. Ji, X., Leng, N., Guo, H.: ‘Progress and Prospects of Underwater Image Enhancement and Restoration Technology’, J. Comput.-Aided Des. Comput. Graph., 2024, 36(6), pp. 805–830 [Google Scholar]
  2. Yu, H., Li, X., Feng, Y., et al.: ‘Underwater vision enhancement based on GAN with dehazing evaluation’, Appl. Intell., 2023, 53, pp. 5664–5680 [Google Scholar]
  3. Xu, H., Long, X., Wang, M.: ‘UUGAN: a GAN-based approach towards underwater image enhancement using non-pairwise supervision’, Int. J. Mach. Learn. Cyber., 2023, 14, pp. 725–738 [Google Scholar]
  4. Li, S., Liu, F., Wei, J.: ‘Two-stage underwater image restoration based on GAN and optical model’, SIViP, 2024, 18, pp. 379–388 [Google Scholar]
  5. Avola, D., et al.: ‘Real-Time GAN-Based Model for Underwater Image Enhancement’, in Foresti, G.L., Fusiello, A., Hancock, E. (Eds.): Image Analysis and Processing - ICIAP 2023 (Lect. Notes Comput. Sci., vol. 14233, Springer, Cham, 2023), pp. 1–7 [Google Scholar]
  6. Zhang, Y., Liu, T., Zhao, B., et al.: ‘SFA-GAN: structure-frequency-aware generative adversarial network for underwater image enhancement’, SIViP, 2023, 17, pp. 3647–3365 [Google Scholar]
  7. Spandana, C., et al.: ‘Underwater Image Enhancement and Restoration Using CycleGAN’, in Hassanien, A.E., et al. (Eds.): International Conference on Innovative Computing and Communications (Lect. Notes Netw. Syst., vol. 537, Springer, Singapore, 2023), pp. 1–9 [Google Scholar]
  8. Kumar, N., Manzar, J., Shivani, et al.: ‘Underwater Image Enhancement using Deep Learning’, Multimed. Tools Appl., 2023, 82, pp. 46789–46809 [Google Scholar]
  9. Li, Y., Li, F., Li, Z.: ‘Multi-scale Attention Conditional GAN for Underwater Image Enhancement’, in Sheng, B., et al. (Eds.): Advances in Computer Graphics (Lect. Notes Comput. Sci., vol. 14495, Springer, Cham, 2024), pp. 1–38 [Google Scholar]
  10. Kong, L., Li, Z., He, X., et al.: ‘Humanlike-GAN: a two-stage asymmetric CycleGAN for underwater image enhancement’, SIViP, 2025, 19, pp. 379 [Google Scholar]
  11. Zhang, G., He, X.: ‘Enhancing Underwater Images Using Improved Cycle GAN Approach’, in Liu, L., et al. (Eds.): Proc. 4th Int. Conf. Auton. Unmanned Syst. (ICAUS 2024) (Lect. Notes Electr. Eng., vol. 1376, Springer, Singapore, 2025), pp. 1–7 [Google Scholar]
  12. McGlamery, B.L.: ‘A computer model for underwater camera systems’, in Proc. Ocean Optics VI, Bellingham, USA, 1980, pp. 221–231 [Google Scholar]
  13. Zhou, L., Liu, Q., Jin, K., et al.: ‘Research Progress on Underwater Image Restoration and Enhancement Methods’, J. Image Graph., 2025, 30(1), pp. 51–65 [Google Scholar]
  14. Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al.: ‘Generative adversarial nets’, Adv. Neural Inf. Process. Syst., 2014, 27, pp. 1–9 [Google Scholar]
  15. Nayak, A.A., Venugopala, P.S., Ashwini, B.: ‘A Systematic Review on Generative Adversarial Network (GAN): Challenges and Future Directions’, Arch. Comput. Methods Eng., 2024, 31, pp. 4739–4772 [Google Scholar]
  16. Zhao, L., Li, Y., Zhong, T.: ‘A generative adversarial network with multi-scale and attention mechanisms for underwater image enhancement’, Sci. Rep., 2025, 15, p. 2787 [Google Scholar]
  17. Yao, F., Zhang, H., Gong, Y., et al.: ‘A study of enhanced visual perception of marine biology images based on diffusion-GAN’, Complex Intell. Syst., 2025, 11, p. 227 [Google Scholar]
  18. Radford, A., Metz, L., Chintala, S.: ‘Unsupervised representation learning with deep convolutional generative adversarial networks’, arXiv:1511.06434, 2015 [Google Scholar]
  19. Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: ‘Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks’, Proc. IEEE Int. Conf. Comput. Vis., 2017, pp. 2223–2232 [Google Scholar]
  20. Liu, X., Liu, Z., Yu, L.: ‘Empower network to comprehend: Semantic guided and attention fusion GAN for underwater image enhancement’, Signal Process. Image Commun., 2025, 134 [Google Scholar]
  21. Feng, J., Han, Y., Pan, C., et al.: ‘AL-GAN: a lightweight GAN underwater image enhancement incorporating attention mechanisms’, J. Ordn. Equip. Eng., 2024, 45(2), pp. 135–143 [Google Scholar]
  22. Liu, H., Lin, S., Lin, Z., et al.: ‘Lightweight Underwater Image Enhancement Network Based on GAN’, Chin. J. Liq. Cryst. Displays, 2023, 38(3), p. 9 [Google Scholar]

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