Underwater Image Enhancement based on Residual and Adversarial Network

Rahman, Md Tosadduk; Tanha, Md. Tawratur Rashid; Hossain, Ishrak

dc.contributor.author	Rahman, Md Tosadduk
dc.contributor.author	Tanha, Md. Tawratur Rashid
dc.contributor.author	Hossain, Ishrak
dc.date.accessioned	2024-09-02T06:29:32Z
dc.date.available	2024-09-02T06:29:32Z
dc.date.issued	2023-05-30
dc.identifier.citation	[1] Z. Fu, X. Lin, W. Wang, Y. Huang, and X. Ding, “Underwater image enhancement via learning water type desensitized representations,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2764–2768, IEEE, 2022. [2] C. Li, C. Guo, W. Ren, R. Cong, J. Hou, S. Kwong, and D. Tao, “An underwater image enhancement benchmark dataset and beyond,” IEEE Transactions on Image Processing, vol. 29, pp. 4376–4389, 2019. [3] C. Li, S. Anwar, J. Hou, R. Cong, C. Guo, and W. Ren, “Underwater image en hancement via medium transmission-guided multi-color space embedding,” IEEE Transactions on Image Processing, vol. 30, pp. 4985–5000, 2021. [4] R. Liu, X. Fan, M. Zhu, M. Hou, and Z. Luo, “Real-world underwater enhancement: Challenges, benchmarks, and solutions under natural light,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 12, pp. 4861–4875, 2020. [5] D. Berman, D. Levy, S. Avidan, and T. Treibitz, “Underwater single image color restoration using haze-lines and a new quantitative dataset,” IEEE transactions on pattern analysis and machine intelligence, vol. 43, no. 8, pp. 2822–2837, 2020. [6] M. J. Islam, Y. Xia, and J. Sattar, “Fast underwater image enhancement for improved visual perception,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3227– 3234, 2020. [7] P. Liu, G. Wang, H. Qi, C. Zhang, H. Zheng, and Z. Yu, “Underwater image en hancement with a deep residual framework,” IEEE Access, vol. 7, pp. 94614–94629, 2019. [8] J. Ao and C. Ma, “Adaptive stretching method for underwater image color cor rection,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 32, no. 02, p. 1854001, 2018. 44 REFERENCES 45 [9] K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE international conference on computer vision, pp. 1026–1034, 2015. [10] P. Wang, P. Chen, Y. Yuan, D. Liu, Z. Huang, X. Hou, and G. Cottrell, “Under standing convolution for semantic segmentation,” in 2018 IEEE winter conference on applications of computer vision (WACV), pp. 1451–1460, Ieee, 2018. [11] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proceedings of the IEEE interna tional conference on computer vision, pp. 2223–2232, 2017. [12] J. Kim, J. K. Lee, and K. M. Lee, “Accurate image super-resolution using very deep convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1646–1654, 2016. [13] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778, 2016. [14] I. Avcibas, B. Sankur, and K. Sayood, “Statistical evaluation of image quality mea sures,” Journal of Electronic imaging, vol. 11, no. 2, pp. 206–223, 2002. [15] B. Lim, S. Son, H. Kim, S. Nah, and K. Mu Lee, “Enhanced deep residual net works for single image super-resolution,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 136–144, 2017. [16] C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, ´ A. Tejani, J. Totz, Z. Wang, et al., “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4681–4690, 2017. [17] A. Hore and D. Ziou, “Image quality metrics: Psnr vs. ssim,” in 2010 20th interna tional conference on pattern recognition, pp. 2366–2369, IEEE, 2010. [18] K. Panetta, C. Gao, and S. Agaian, “Human-visual-system-inspired underwater image quality measures,” IEEE Journal of Oceanic Engineering, vol. 41, no. 3, pp. 541–551, 2015. [19] P. M. Uplavikar, Z. Wu, and Z. Wang, “All-in-one underwater image enhancement using domain-adversarial learning.,” in CVPR workshops, pp. 1–8, 2019. REFERENCES 46 [20] C. Ancuti, C. O. Ancuti, T. Haber, and P. Bekaert, “Enhancing underwater images and videos by fusion,” in 2012 IEEE conference on computer vision and pattern recognition, pp. 81–88, IEEE, 2012. [21] X. Fu, P. Zhuang, Y. Huang, Y. Liao, X.-P. Zhang, and X. Ding, “A retinex-based enhancing approach for single underwater image,” in 2014 IEEE international con ference on image processing (ICIP), pp. 4572–4576, IEEE, 2014. [22] Y.-T. Peng, K. Cao, and P. C. Cosman, “Generalization of the dark channel prior for single image restoration,” IEEE Transactions on Image Processing, vol. 27, no. 6, pp. 2856–2868, 2018. [23] C.-Y. Li, J.-C. Guo, R.-M. Cong, Y.-W. Pang, and B. Wang, “Underwater image enhancement by dehazing with minimum information loss and histogram distribu tion prior,” IEEE Transactions on Image Processing, vol. 25, no. 12, pp. 5664–5677, 2016. [24] Y.-T. Peng and P. C. Cosman, “Underwater image restoration based on image blur riness and light absorption,” IEEE transactions on image processing, vol. 26, no. 4, pp. 1579–1594, 2017. [25] C. Li, J. Guo, and C. Guo, “Emerging from water: Underwater image color cor rection based on weakly supervised color transfer,” IEEE Signal processing letters, vol. 25, no. 3, pp. 323–327, 2018. [26] Y. Guo, H. Li, and P. Zhuang, “Underwater image enhancement using a multi scale dense generative adversarial network,” IEEE Journal of Oceanic Engineering, vol. 45, no. 3, pp. 862–870, 2019. [27] R. Timofte, S. Gu, J. Wu, L. Van Gool, L. Zhang, M.-H. Yang, M. Haris, et al., “Ntire 2018 challenge on single image super-resolution: Methods and results,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2018. [28] X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, Y. Qiao, and C. Change Loy, “Esr gan: Enhanced super-resolution generative adversarial networks,” in Proceedings of the European conference on computer vision (ECCV) workshops, pp. 0–0, 2018. [29] K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European conference on computer vision, pp. 630–645, Springer, 2016. REFERENCES 47 [30] S. Hochreiter, “The vanishing gradient problem during learning recurrent neural nets and problem solutions,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 6, no. 02, pp. 107–116, 1998. [31] S. Santurkar, D. Tsipras, A. Ilyas, and A. Madry, “How does batch normalization help optimization?,” Advances in neural information processing systems, vol. 31, 2018. [32] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM, vol. 63, no. 11, pp. 139–144, 2020	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/2151
dc.description	Supervised by Dr. Md. Hasanul Kabir, Professor, Shahriar Ivan, Lecturer, Md. Zahidul Islam, Lecturer, Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.description.abstract	Due to the complexity and constraints of the underwater environment, underwa ter picture enhancement is a difficult task. In order to improve underwater images that have problems with low contrast, blurriness, and color mistakes, this research suggests a deep learning-based technique. Residual Networks (ResNet) and Super Resolution Generative Adversarial Networks (SRGANs) are combined in the sug gested method. In order to restore fine details and improve overall contrast and sharpness, ResNet extracts residual information. SRGANs produce enhanced under water picture versions at high resolution, enhancing visual integrity. Extensive testing on several underwater picture datasets reveals the suggested method’s superior performance. Comparing it to cutting-edge methods, objective quality indicators such as contrast augmentation, image sharpness, and color accu racy confirm its efficiency. Qualitative evaluations show that the underwater pho tographs have significantly improved in terms of contrast, blurriness, and color re production. This increases their ability to be analyzed and interpreted as well as their visual appeal. Marine research, underwater robots, and inspection systems can all benefit from better underwater image quality. Improved visual quality is bene ficial for accurate underwater object identification, biodiversity measurement, and extending our understanding of underwater ecosystems. In conclusion, this study provides a deep learning-based technique for enhancing underwater image quality that combines ResNet and SRGANs. The method addresses low contrast, blurriness, and color mistakes to produce notable improvements. Its effectiveness is supported by the experimental findings and qualitative evaluations, emphasizing its potential to advance underwater photography methods and applications	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Underwater Image Enhancement based on Residual and Adversarial Network	en_US
dc.type	Thesis	en_US