Quality Enhancement for Underwater Images using Various Image Processing Techniques: A Survey
Article Sidebar
Main Article Content
Abstract
Underwater images are essential to identify the activity of underwater objects. It played a vital role to explore and utilizing aquatic resources. The underwater images have features such as low contrast, different noises, and object imbalance due to lack of light intensity. CNN-based in-deep learning approaches have improved underwater low-resolution photos during the last decade. Nevertheless, still, those techniques have some problems, such as high MSE, PSNT and high SSIM error rate. They solve the problem using different experimental analyses; various methods are studied that effectively treat different underwater image distorted scenes and improve contrast and color deviation compared to other algorithms. In terms of the color richness of the resulting images and the execution time, there are still deficiencies with the latest algorithm. In future work, the structure of our algorithm will be further adjusted to shorten the execution time, and optimization of the color compensation method under different color deviations will also be the focus of future research. With the wide application of underwater vision in different scientific research fields, underwater image enhancement can play an increasingly significant role in the process of image processing in underwater research and underwater archaeology. Most of the target images of the current algorithms are shallow water images. When the artificial light source is added to deep water images, the raw images will face more diverse noises, and image enhancement will face more challenges. As a result, this study investigates the numerous existing systems used for quality enhancement of underwater mages using various image processing techniques. We find various gaps and challenges of current systems and build the enhancement of this research for future improvement. Aa a result of this overview is to define the future problem statement to enhance this research and overcome the challenges faced by previous researchers. On other hand also improve the accuracy in terms of reducing MSE and enhancing PSNR etc.
Article Details
References
Sinha, G. R., Kavita Thakur, and M. K. Kowar. "Contrast Enhancement of Underwater Images." Journal of Acoustical. Society of India 35.1 (2008): 33-35.
Bai, Linfeng, et al. "Underwater image enhancement based on global and local equalization of histogram and dual-image multi-scale fusion." IEEE Access 8 (2020): 128973-128990.
Liu, Peng, et al. "Underwater image enhancement with a deep residual framework." IEEE Access 7 (2019): 94614-94629.
Garg, Diksha, Naresh Kumar Garg, and Munish Kumar. "Underwater image enhancement using blending of CLAHE and percentile methodologies." Multimedia Tools and Applications 77.20 (2018): 26545-26561.
Dhivya, R., R. Praka1sh, and M. R. Mohanraj. "Color Balance and Fusion for Underwater Image Enhancement." Digital Image Processing 11.2 (2019): 25-29.
Khan, Amjad, et al. "Underwater image enhancement by wavelet-based fusion." 2016 IEEE International Conference on Underwater System Technology: Theory and Applications (USYS). IEEE, 2016.
Peng, Yan-Tsung, Xiangyun Zhao, and Pamela C. Cosman. "Single underwater image enhancement using depth estimation
based on blurriness." 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015.
Fu, Xueyang, et al. "A retinex-based enhancing approach for single underwater image." 2014 IEEE international conference on image processing (ICIP). IEEE, 2014.
Hitam, Muhammad Suzuri, et al. "Mixture contrast limited adaptive histogram equalization for underwater image enhancement." 2013 International conference on computer applications technology (ICCAT). IEEE, 2013.
Chiang, John Y., and Ying-Ching Chen. "Underwater image enhancement by wavelength compensation and dehazing." IEEE transactions on image processing 21.4 (2011): 1756-1769.
Yang, Hung-Yu, et al. "Low complexity underwater image enhancement based on dark channel prior." 2011 Second International Conference on Innovations in Bio-inspired Computing and Applications. IEEE, 2011.
Iqbal, Kashif, et al. "Enhancing the low-quality images using unsupervised colour correction method." 2010 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2010.
Petit, Frédéric, Anne-Sophie Capelle-Laizé, and Philippe Carré. "Underwater image enhancement by attenuation inversionwith quaternions." 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009.
Christie, S. M., and F. Kvasnik. "Contrast enhancement of underwater images with coherent optical image processors." Applied optics 35.5 (1996): 817-825.
Trucco, Emanuele, and Adriana T. Olmos-Antillon. "Self-tuning underwater image restoration." IEEE Journal of Oceanic Engineering 31.2 (2006): 511-519.
Gunjal, M. B. ., & Sonawane, V. R. . (2023). Multi Authority Access Control Mechanism for Role Based Access Control for Data Security in the Cloud Environment. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 250 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2623
Iqbal, Kashif, et al. "Underwater Image Enhancement Using an Integrated Color Model." IAENG International Journal of computer science 34.2 (2007).
Yang, Miao, and Arcot Sowmya. "An underwater color image quality evaluation metric." IEEE Transactions on Image Processing 24.12 (2015): 6062-6071.
Mr. Ather Parvez Abdul Khalil. (2012). Healthcare System through Wireless Body Area Networks (WBAN) using Telosb Motes. International Journal of New Practices in Management and Engineering, 1(02), 01 - 07. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/4
Li, Chong-Yi, et al. "Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior." IEEE Transactions on Image Processing 25.12 (2016): 5664-5677.
Wang, Yan, et al. "An experimental-based review of image enhancement and image restoration methods for underwater imaging." IEEE Access 7 (2019): 140233-140251.
Tao, Ye, Lili Dong, and Wenhai Xu. "A Novel Two-Step Strategy Based on White-Balancing and Fusion for Underwater Image Enhancement." IEEE Access 8 (2020): 217651-217670.
Chen, Weiling, et al. "Semi-reference sonar image quality assessment based on task and visual perception." IEEE Transactions on Multimedia 23 (2020): 1008-1020.
Murino, Vittorio, and Andrea Trucco. "A confidence-based approach to enhancing underwater acoustic image formation." IEEE transactions on image processing 8.2 (1999): 270-285.
Yang, Shudi, et al. "Underwater image enhancement using scene depth-based adaptive background light estimation and dark channel prior algorithms." IEEE Access 7 (2019): 165318-165327.
Han, Ruyue, et al. "Underwater Image Enhancement Based on a Spiral Generative Adversarial Framework." IEEE Access 8 (2020): 218838-218852.
Mondal , D. (2021). Green Channel Roi Estimation in The Ovarian Diseases Classification with The Machine Learning Model . Machine Learning Applications in Engineering Education and Management, 1(1), 07–12.
Thomas Wilson, Andrew Evans, Alejandro Perez, Luis Pérez, Juan Martinez. Integrating Machine Learning and Decision Science for Effective Risk Management. Kuwait Journal of Machine Learning, 2(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/208
Li, Yujie, et al. "Underwater image high definition display using the multilayer perceptron and color feature-based srcnn." IEEE Access 7 (2019):83721-83728
substrate interface,” IEEE Transl. J. Magn. Japan, vol. 2, pp. 740–741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].
M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989.
Electronic Publication: Digital Object Identifiers (DOIs):
Article in a journal:
D. Kornack and P. Rakic, “Cell Proliferation without Neurogenesis in Adult Primate Neocortex,” Science, vol. 294, Dec. 2001, pp. 2127-2130, doi:10.1126/science.1065467.
Article in a conference proceeding:
H. Goto, Y. Hasegawa, and M. Tanaka, “Efficient Scheduling Focusing on the Duality of MPL Representatives,” Proc. IEEE Symp. Computational Intelligence in Scheduling (SCIS 07), IEEE Press, Dec. 2007, pp. 57-64, doi:10.1109/SCIS.2007.357