Low Intricacy‎ Multistage Algorithm for Underwater Image Enhancement

Zohair Al-Ameen, Ahmed A. Ahmed

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.

Keywords


underwater; concepts; image enhancement image processing; color images

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Lavy, A., Eyal, G., Neal, B., Keren, R., Loya, Y., & Ilan, M. (2015). A quick, easy and non‐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.‏

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.‏

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.‏

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.‏

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.‏

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.‏

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.‏

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.‏

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

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).‏

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.‏

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.‏

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.‏

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.‏

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

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.‏

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). Fusion‐based simultaneous estimation of reflectance and illumination for low‐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.




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

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