Browse > Article
http://dx.doi.org/10.3837/tiis.2022.08.005

Recovery of underwater images based on the attention mechanism and SOS mechanism  

Li, Shiwen (School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications)
Liu, Feng (School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications)
Wei, Jian (School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.8, 2022 , pp. 2552-2570 More about this Journal
Abstract
Underwater images usually have various problems, such as the color cast of underwater images due to the attenuation of different lights in water, the darkness of image caused by the lack of light underwater, and the haze effect of underwater images because of the scattering of light. To address the above problems, the channel attention mechanism, strengthen-operate-subtract (SOS) boosting mechanism and gated fusion module are introduced in our paper, based on which, an underwater image recovery network is proposed. First, for the color cast problem of underwater images, the channel attention mechanism is incorporated in our model, which can well alleviate the color cast of underwater images. Second, as for the darkness of underwater images, the similarity between the target underwater image after dehazing and color correcting, and the image output by our model is used as the loss function, so as to increase the brightness of the underwater image. Finally, we employ the SOS boosting module to eliminate the haze effect of underwater images. Moreover, experiments were carried out to evaluate the performance of our model. The qualitative analysis results show that our method can be applied to effectively recover the underwater images, which outperformed most methods for comparison according to various criteria in the quantitative analysis.
Keywords
underwater image; deep learning; gated module; dehazing; attention mechanism;
Citations & Related Records
연도 인용수 순위
  • Reference
1 S. Zhang and T. Wang et al., "Underwater image enhancement via extended multi-scale retinex," Neurocomputing, vol. 245, pp. 1-9, 2017.   DOI
2 C. Li, S. Anwar, and F. Porikli, "Underwater scene prior inspired deep underwater image and video enhancement," Pattern Recogn, vol. 98, 2020.
3 S. Anwar, C. Li, and F. Porikli, "Deep Underwater Image Enhancement," ArXiv180703528, Jul. 2018.
4 X. Chen and J. Yu et al., "Towards real-time advancement of underwater visual quality with gan," IEEE Transactions on Ind. Electron, vol. 66, no. 12, pp. 9350 - 9359, 2019.   DOI
5 H. Dong and J. Pan et al., "Multi-scale boosted dehazing network with dense feature fusion," in Proc. of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2157-2167, 2020.
6 Y. Romano and M. Elad, "Boosting of image denoising algorithms," SIAM Journal on Imaging Sciences, vol. 8, no. 2, pp. 1187-1219, Jan. 2015.   DOI
7 L. Chen, Q. S. Sun, and F. Wang, "Attention-adaptive and deformable convolutional modules for dynamic scenedeblurring," Information Sciences, vol. 546, pp. 368-377, 2021.   DOI
8 B. Cai, X. Xu, and K. Guo, "A joint intrinsic extrinsic prior model for retinex," in Proc. of 2017 IEEE Int. Conf. on Comput. Vis. (ICCV), pp. 4000-4009, 2017.
9 X. Fu and P. Zhuang et al., "A retinex-based enhancing approach for single underwater image," in Proc. of 2014 IEEE International Conference on Image Processing (ICIP), pp. 4572-4576, 2014.
10 C. Fabbri, M. Islam, and J. Sattar, "Enhancing underwater imagery using generative adversarial networks," in Proc. of 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 7159-7165, 2018.
11 C., Li, C. Guo, W. Ren, R. Cong, et al., "An underwater image enhancement benchmark dataset and beyond," IEEE Transactions on Image Processing, vol. 29, pp. 4376-4389, 2020.   DOI
12 X. Liu, Z. Gao, and B. M. Chen, "IPMGAN: integrating physical model and gener- ative adversarial network forunderwater image enhancement," Neurocomputing, vol. 453, pp. 538-551, 2021.   DOI
13 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, Dec. 2020.   DOI
14 Z. Liang, Y. Wang, X. Ding, Z. Mi, and X. Fu, "Single underwater image enhancement by attenuation map guided color correction and detail preserved dehazing," Neurocomputing, vol. 425, no. 15, pp. 160-172, 2021.   DOI
15 J. Xu, Y. Hou, and D. Ren et al., "Star: A structure and texture aware retinex model," IEEE Transactions on ImageProcess, vol. 29, pp. 5022-5037, 2020.   DOI
16 C. Ancuti, C. O. Ancuti, T. Haber, P. Bekaert, "Enhancing underwater images and videos by fusion," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 81-88, 2012.
17 K. He, S. Jian, and X. Tang, "Single image haze removal using dark channel prior," Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2341-2353, Dec. 2011.   DOI
18 M. J. Islam and Y. Xia et al., "Fast underwater image enhancement for improved visual perception," IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3227-3234, April 2020.   DOI
19 M. Li, J. Liu, and W. Yang et al., "Structure-revealing low-light image enhancement via robust retinex model," IEEE Transactions on Image Processing, vol. 27, no. 6, pp. 2828-2841, June 2018.   DOI
20 L. Chao and M. Wang, "Removal of water scattering," in Proc. of 2010 2nd International Conference on Computer Engineering and Technology, vol. 2, pp. V2-35-V2-39, 2010.
21 Y. Zhou, K. Yan and X. Li, "Underwater Image Enhancement via Physical-Feedback Adversarial Transfer Learning," IEEE Journal of Oceanic Engineering, vol. 47, no. 1, pp. 76-87, Jan. 2022.   DOI
22 K. Zuiderveld, "Contrast limited adaptive histogram equalization," Graph. Gems, vol. 38, pp. 474-485, 1994.   DOI
23 X. Fu and X. Cao, "Underwater image enhancement with global-local networks and compressed-histogram equalization," Signal Processing: Image Communication, vol. 86, 2020.
24 D. Berman, D. Levy, and S. Avidan et al., "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, 1 Aug. 2021.
25 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, July 2016.   DOI
26 M. Yang and A. Sowmya, "An Underwater Color Image Quality Evaluation Metric," IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 6062-6071, Dec. 2015.   DOI
27 D. Chen and M. He et al., "Gated context aggregation network for image dehazing and deraining," in Proc. of 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1375-1383, 2019.
28 W. Song, Y. Wang, D. Huang, A. Liotta, and C. Perra, "Enhancement of underwater images with statistical model of background light and optimization of transmission map," IEEE Transactions on Broadcasting, vol. 66, no. 1, pp. 153-169, March 2020.   DOI
29 J. Zhou, T. Yang, W. Ren, D. Zhang, and W. Zhang, "Underwater image restoration via depth map and illumination estimation based on a single image," Opt. Express, vol. 29, no. 18, pp. 29864-29886, 2021.   DOI
30 R. Hummel, "Image enhancement by histogram transformation," Comput. Graph. Image Process., pp. 184-195, 1975.