Low Intricacy‎ Multistage Algorithm for Underwater Image Enhancement

Authors

  • Zohair Al-Ameen Department of Computer Science, College of Computer Science and Mathematics, University of Mosul, Iraq
  • Ahmed A. Ahmed Department of Computer Science, College of Computer Science and Mathematics, University of Mosul, Iraq

DOI:

https://doi.org/10.33387/protk.v11i1.6888

Keywords:

underwater, concepts, image enhancement image processing, color images

Abstract

Humanity currently lives in a technological era that witnesses rapid progress in multiple fields. Digital image processing is one of the modern technologies that has provided practical answers to many challenges including image enhancement, analysis, reconstruction, recovery, compression, processing, and understanding. One of these notable challenges relates to underwater photography. Underwater images are always exposed to less-than-ideal conditions due to environmental and physical factors. These include refraction of light in water, scattering of particles and dust in the aquatic medium, lack of illumination in deep water, and poor contrast. These challenges make it extremely difficult to analyze and extract valuable information without advanced processing.  In this study, an improved color balance-fusion algorithm is provided by improving the image visuality and modifying some equations to obtain sharper and clearer images. The proposed algorithm begins by finding the white balance of the input RGB color image, after that, it improves the intensity. Next, the edges are improved using Gamma separately. The weights are then found for each image and combined to find naive fusion. The resulting image is processed using a color retrieval algorithm to produce the final image. along with comparisons to eleven other algorithms with various processing methods. Experimental results showed that this algorithm can significantly improve underwater images, increasing image clarity and making colors clearer. The improvement rates reached 5.8389 and 2.6778 for UISM and UICM metrics, respectively.

Downloads

Download data is not yet available.

References

Lavy, A., Eyal, G., Neal, B., Keren, R., Loya, Y., & Ilan, M. (2015). A quick, easy and nonâ€intrusive method for underwater volume and surface area evaluation of benthic organisms by 3D computer modelling. Methods in Ecology and Evolution, 6(5), 521-531.â€

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.â€

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.â€

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

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.â€

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.â€

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.â€

Butler, J., Stanley, J. A., & Butler IV, M. J. (2016). Underwater soundscapes in near-shore tropical habitats and the effects of environmental degradation and habitat restoration. Journal of Experimental Marine Biology and Ecology, 479, 89-96.

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.â€

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.â€

Chiang, J. Y., & Chen, Y. C. (2012). Underwater image enhancement by wavelength compensation and dehazing. IEEE Transactions on Image Processing, 21(4), 1756-1769.â€

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).â€

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.â€

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.â€

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.â€

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.â€

Li, X., Hou, G., Tan, L., & Liu, W. (2020). A hybrid framework for underwater image enhancement. IEEE Access, 8, 197448-197462.â€

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.

Wang, S., Chen, Z., & Wang, H. (2022). Multi-weight and multi-granularity fusion of underwater image enhancement. Earth Science Informatics, 15(3), 1647-1657.â€

Ancuti, C. O., Ancuti, C., De Vleeschouwer, C., & Bekaert, P. (2017). Color balance and fusion for underwater image enhancement. IEEE Transactions on image processing, 27(1), 379-393.

Goldstein, E. B. (Ed.). (2009). Encyclopedia of perception. Sage.

Huang, Z., Fang, H., Li, Q., Li, Z., Zhang, T., Sang, N., & Li, Y. (2018). Optical remote sensing image enhancement with weak structure preservation via spatially adaptive gamma correction. Infrared Physics & Technology, 94, 38-47.

Yang, X., Yin, C., Zhang, Z., Li, Y., Liang, W., Wang, D., ... & Fan, H. (2020). Robust chromatic adaptation based color correction technology for underwater images. Applied Sciences, 10(18), 6392.

Ma, S., Hanselaer, P., Teunissen, K., & Smet, K. A. (2020). Effect of adapting field size on chromatic adaptation. Optics Express, 28(12), 17266-17285.

Rahman, S., Rahman, M. M., Abdullah-Al-Wadud, M., Al-Quaderi, G. D., & Shoyaib, M. (2016). An adaptive gamma correction for image enhancement. EURASIP Journal on Image and Video Processing, 2016(1), 1-13.

Rao, N., Venkatasekhar, D., Venkatramana, P., & Usha Rani, C. (2019). Optical Features Based Fusion Principle for Under Water Image Enhancement. Journal of Computational and Theoretical Nanoscience, 16(4), 1227-1233.

Liu, Z., Song, S., Wang, B., Gong, W., Ran, Y., Hou, X., ... & Li, F. (2022). Multispectral LiDAR point cloud highlight removal based on color information. Optics Express, 30(16), 28614-28631.

Parihar, A. S., Singh, K., Rohilla, H., & Asnani, G. (2021). Fusionâ€based simultaneous estimation of reflectance and illumination for lowâ€light image enhancement. IET Image Processing, 15(7), 1410-1423.

Al-Ameen, Z. (2020). Satellite Image Enhancement Using an Ameliorated Balance Contrast Enhancement Technique. Traitement du Signal, 37(2).

Al-Ameen, Z., Al-Healy, M. A., & Hazim, R. A. (2020). Anisotropic Diffusion-Based Unsharp Masking for Sharpness Improvement in Digital Images. Journal of Soft Computing & Decision Support Systems, 7(1).

Zhang, W., Dong, L., Pan, X., Zhou, J., Qin, L., & Xu, W. (2019). Single image defogging based on multi-channel convolutional MSRCR. IEEE Access, 7, 72492-72504.

Huang, Z., Fang, H., Li, Q., Li, Z., Zhang, T., Sang, N., & Li, Y. (2018). Optical remote sensing image enhancement with weak structure preservation via spatially adaptive gamma correction. Infrared Physics & Technology, 94, 38-47.

Li, C., Guo, C., Ren, W., Cong, R., Hou, J., Kwong, S., & Tao, D. (2019). An underwater image enhancement benchmark dataset and beyond. IEEE Transactions on Image Processing, 29, 4376-4389.

Panetta, K., Gao, C., & Agaian, S. (2015). Human-visual-system-inspired underwater image quality measures. IEEE Journal of Oceanic Engineering, 41(3), 541-551.

Downloads

Additional Files

Published

2024-01-02

How to Cite

Al-Ameen, Z., & Ahmed, A. A. (2024). Low Intricacy‎ Multistage Algorithm for Underwater Image Enhancement. Protek : Jurnal Ilmiah Teknik Elektro, 11(1), 1–12. https://doi.org/10.33387/protk.v11i1.6888

Issue

Vol. 11 No. 1 (2024): PROtek : Jurnal Ilmiah Teknik Elektro

Section

Electrical, Power and Energy

License

Copyright & License

Copyright, licensing, and reuse terms for articles published in Protek: Jurnal Ilmiah Teknik Elektro

2. License

Creative Commons License CC BY-NC-SA 4.0 Articles in Protek are published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Anyone may share and adapt the work under the following terms:

BY
Attribution
Give appropriate credit to the authors and the journal, link to the license, and indicate if changes were made.
NC
NonCommercial
The material may not be used for commercial purposes.
SA
ShareAlike
Adaptations must be distributed under the same license as the original.

↑ Back to Top

3. Author's Warranties

By submitting a manuscript, the author warrants that the article:

  • is original and has not been published before;
  • contains no unlawful statements;
  • does not infringe the rights of others;
  • is subject to copyright vested exclusively in the author(s); and
  • includes any necessary permissions to quote material from other sources.

↑ Back to Top

4. User Rights

Under CC BY-NC-SA 4.0, users may copy, distribute, display, and perform the work for non-commercial purposes only. When distributing the work, users must give proper credit to the authors and to Protek as the original place of publication.

↑ Back to Top

5. Rights of Authors

Authors retain all their rights to the published work, including, but not limited to:

  • Copyright and other proprietary rights relating to the article, such as patent rights;
  • Reuse of the substance of the article in their own future works, including lectures and books;
  • Reproduction of the article for their own purposes;
  • Self-archiving of the article, for example in institutional repositories or personal websites;
  • Separate agreements for the non-exclusive distribution of the article, with an acknowledgment of its first publication in Protek.

↑ Back to Top

6. Co-Authorship

If the article was prepared jointly with other authors, the submitting author warrants that they have been authorized by all co-authors to agree to these terms on their behalf, and agrees to inform the co-authors of these terms. Protek communicates only with the corresponding author and is not responsible for disputes among the authors.

↑ Back to Top

7. Royalties

The parties agree that no royalties or other fees are paid to the authors for publication. To the extent legally permissible, authors waive any claim to royalties for the publication of their article.

↑ Back to Top

8. Miscellaneous

  • Protek publishes the article in electronic form and makes it freely available to the public, without charge to readers.
  • The editors may modify the article to conform to the journal's style, including punctuation, spelling, capitalization, referencing, and usage.

Questions about copyright or licensing can be sent to protek@unkhair.ac.id. See also the Open Access Policy and Publication Ethics.

↑ Back to Top

Similar Articles

1 2 3 > >> 

You may also start an advanced similarity search for this article.