Underwater Image Enhancement: A Review of Modern Utilized Concepts

Abstract

The revolution in current technology imposes on most systems of daily life in processing a wide range of applications through digital image processing techniques that have produced practical answers to many challenges such as image optimization, analysis, reconstruction, recovery, compression, processing, and so forth. One application of interest in underwater image enhancement has attracted the attention of experts in recent years due to its importance. In this work, a review of modern processing concepts for underwater images is given to get the basic knowledge on what are the used methodologies, what are the processed degradations, and what processing concepts have been used in the past years. Hence, different methods are reviewed and implemented on underwater images to know their advantages and disadvantages. The results of each method are demonstrated, the processing concept is summarized, and a synopsis that includes the main notion, complexity, pros, and cons is given to get a generalized idea about this topic.

Country : Iraq

1 Ahmed A. Ahmed2 Zohair Al-Ameen

  1. Department of Computer Science, College of Computer Science and Mathematics, University of Mosul, Iraq
  2. Department of Computer Science, College of Computer Science and Mathematics, University of Mosul, Iraq

IRJIET, Volume 7, Issue 8, August 2023 pp. 167-172

doi.org/10.47001/IRJIET/2023.708021

References

  1. Lavy, A., Eyal, G., Neal, B., Keren, R., Loya, Y., & Ilan, M. (2015). A quick, easy and nonintrusive method for underwater volume and surface area evaluation of benthic organisms by 3D computer modelling. Methods in Ecology and Evolution, 6(5), 521-531.
  2. Pacheco-Ruiz, R., Adams, J., & Pedrotti, F. (2018). 4D modelling of low visibility Underwater Archaeological excavations using multi-source photogrammetry in the Bulgarian Black Sea. Journal of Archaeological Science, 100, 120-129.
  3. Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., & He, L. (2022). A survey of human-in-the-loop for machine learning. Future Generation Computer Systems, 135, 364-381.
  4. Dewangan, S. K. (2017, May). Visual quality restoration & enhancement of underwater images using HSV filter analysis. In 2017 International Conference on Trends in Electronics and Informatics (ICEI) (pp. 766-772). IEEE.
  5. Hambarde, P., Murala, S., & Dhall, A. (2021). UW-GAN: Single-image depth estimation and image enhancement for underwater images. IEEE Transactions on Instrumentation and Measurement, 70, 1-12.
  6. Rajasekar, M., Celine Kavida, A., & Anto Bennet, M. (2020). A pattern analysis based underwater video segmentation system for target object detection. Multidimensional Systems and Signal Processing, 31, 1579-1602.
  7. Chiang, J. Y., & Chen, Y. C. (2011). Underwater image enhancement by wavelength compensation and dehazing. IEEE transactions on image processing, 21(4), 1756-1769.
  8. Liu, Y., Xu, H., Shang, D., Li, C., & Quan, X. (2019). An underwater image enhancement method for different illumination conditions based on color tone correction and fusion-based descattering. Sensors, 19(24), 5567.
  9. Butler, J., Stanley, J. A., & Butler IV, M. J. (2016). Underwater sounds capes in near-shore tropical habitats and the effects of environmental degradation and habitat restoration. Journal of Experimental Marine Biology and Ecology, 479, 89-96.
  10. Liu, X., Guillén, I., La Manna, M., Nam, J. H., Reza, S. A., Huu Le, T., ... & Velten, A. (2019). Non-line-of-sight imaging using phasor-field virtual wave optics. Nature, 572(7771), 620-623.
  11. Schöntag, P., Nakath, D., Röhrl, S., & Köser, K. (2022, May). Towards Cross Domain Transfer Learning for Underwater Correspondence Search. In International Conference on Image Analysis and Processing (pp. 461-472). Cham: Springer International Publishing.
  12. Zhou, J., Sun, J., Zhang, W., & Lin, Z. (2023). Multi-view underwater image enhancement method via embedded fusion mechanism. Engineering Applications of Artificial Intelligence, 121, 105946.
  13. Fayaz, S., Parah, S. A., Qureshi, G. J., & Kumar, V. (2021). Underwater image restoration: A stateoftheart review. IET Image Processing, 15(2), 269-285.
  14. Yang, M., Yin, G., Wang, H., Dong, J., Xie, Z., & Zheng, B. (2022). A underwater sequence image dataset for sharpness and color analysis. Sensors, 22(9), 3550.
  15. Zhang, D., Wu, C., Zhou, J., Zhang, W., Li, C., & Lin, Z. (2023). Hierarchical attention aggregation with multi-resolution feature learning for GAN-based underwater image enhancement. Engineering Applications of Artificial Intelligence, 125, 106743.
  16. Drews, P., Nascimento, E., Moraes, F., Botelho, S., & Campos, M. (2013). Transmission estimation in underwater single images. In Proceedings of the IEEE international conference on computer vision workshops (pp. 825-830).
  17. Peng, Y. T., & Cosman, P. C. (2017). Underwater image restoration based on image blurriness and light absorption. IEEE Transactions on Image Processing, 26(4), 1579-1594.
  18. Fu, X., Fan, Z., Ling, M., Huang, Y., & Ding, X. (2017, November). Two-step approach for single underwater image enhancement. In 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) (pp. 789-794). IEEE.
  19. Pan, P. W., Yuan, F., & Cheng, E. (2018). Underwater image de-scattering and enhancing using dehazenet and HWD. Journal of Marine Science and Technology, 26(4), 6.
  20. Bavirisetti, D. P., Xiao, G., Zhao, J., Dhuli, R., & Liu, G. (2019). Multi-scale guided image and video fusion: A fast and efficient approach. Circuits, Systems, and Signal Processing, 38, 5576-5605.
  21. Li, X., Hou, G., Tan, L., & Liu, W. (2020). A hybrid framework for underwater image enhancement. IEEE Access, 8, 197448-197462.
  22. Fayaz, S., Parah, S. A., & Qureshi, G. J. (2023). Efficient underwater image restoration utilizing modified dark channel prior. Multimedia Tools and Applications, 82(10), 14731-14753.
  23. Wang, S., Chen, Z., & Wang, H. (2022). Multi-weight and multi-granularity fusion of underwater image enhancement. Earth Science Informatics, 15(3), 1647-1657.