Underwater Image Enhancement Based on UNDERWATER-CUT Model

YAO Peng; LIU Yu-hui

doi:10.11993/j.issn.2096-3920.202111004

Volume 30 Issue 5

Oct 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Unmanned Undersea Systems > 2022 > 30(5): 605-611

YAO Peng, LIU Yu-hui. Underwater Image Enhancement Based on UNDERWATER-CUT Model[J]. Journal of Unmanned Undersea Systems, 2022, 30(5): 605-611. doi: 10.11993/j.issn.2096-3920.202111004

Citation:

YAO Peng, LIU Yu-hui. Underwater Image Enhancement Based on UNDERWATER-CUT Model[J]. Journal of Unmanned Undersea Systems, 2022, 30(5): 605-611. doi: 10.11993/j.issn.2096-3920.202111004

YAO Peng, LIU Yu-hui. Underwater Image Enhancement Based on UNDERWATER-CUT Model[J]. Journal of Unmanned Undersea Systems, 2022, 30(5): 605-611. doi: 10.11993/j.issn.2096-3920.202111004

Citation:

YAO Peng, LIU Yu-hui. Underwater Image Enhancement Based on UNDERWATER-CUT Model[J]. Journal of Unmanned Undersea Systems, 2022, 30(5): 605-611. doi: 10.11993/j.issn.2096-3920.202111004

PDF( 3762 KB)

Underwater Image Enhancement Based on UNDERWATER-CUT Model

doi: 10.11993/j.issn.2096-3920.202111004

College of Engineering, Ocean University of China, Qingdao 266100, China

Received Date: 2021-11-16
Accepted Date: 2022-08-12
Rev Recd Date: 2022-01-21

Available Online: 2022-09-05

Abstract

Abstract

A weakly supervised underwater image enhancement algorithm based on the UNDERWATER-CUT model was proposed to address color distortion and contrast imbalance in underwater images. The algorithm network was trained without paired training sets, and the contrast learning positive and negative samples were constructed by image chunking, which constrains the image generation content. The structural similarity(SSIM) loss function was used to constrain the image enhancement to ensure that the structure of the objects remains unchanged during the transformation of the CUT model from the underwater image domain to the dewatered image domain. A linear combination of two simple functions was used to approximate the InfoNCE loss of the CUT model, converging the improved model training to the optimal value easily. The experimental results demonstrate that color distortion is significantly corrected in the enhanced image using this algorithm, and the object structure in the restored image is the same as that in the original image.
- underwater image enhancement,
- structural similarity,
- InfoNCE loss function

FullText(HTML)

References(19)

References

[1]	杨爱萍, 曲畅, 王建, 等. 基于水下成像模型的图像清晰化算法[J]. 电子与信息学报, 2018, 40(2): 298-305. Yang Ai-ping, Qu Chang, Wang Jian, et al. Underwater Image Visibility Restoration Based on Underwater Imaging Model[J]. Journal of Electronics & Information Technology, 2018, 40(2): 298-305.
[2]	He K, Sun J, Tang X. Single Image Haze Removal Using Dark Channel Prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 33(12): 2341-2353.
[3]	Wen H, Tian Y, Huang T, et al. Single Underwater Image Enhancement with a New Optical Model[C]//2013 IEEE International Symposium on Circuits and Systems(ISCAS). Beijing, China: IEEE, 2013: 753-756.
[4]	Li J, Skinner K A, Eustice R M, et al. WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images[J]. IEEE Robotics and Automation Letters, 2017, 3(1): 387-394.
[5]	Li C, Guo J, Guo C. Emerging from Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer[J]. IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers Inc., 2018, 25(3): 323-327.
[6]	Zhu J Y, Park T, Isola P, et al. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks[C]//2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE, 2017.
[7]	Wang Z, Bovik A C, Sheikh H R, et al. Image Quality Assessment: from Error Visibility to Structural Similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612. doi: 10.1109/TIP.2003.819861
[8]	李庆忠, 白文秀, 牛炯. 基于改进CycleGAN的水下图像颜色校正与增强[J/OL]. 自动化学报, 2020, 46(x): 1-11.[2021-10-10]. http://www.aas.net.cn/cn/article/doi/10.16383/j.aas.c200510. Li Qing-Zhong, Bai Wen-Xiu, Niu Jiong. Underwater Image Color Correction and Enhancement Based on Improved Cycle-consistent Generative Adversarial Networks[J/OL]. Acta Automatica Sinica, 2020, 46(x): 1−11. [2021-10-10]. http://www.aas.net.cn/cn/article/doi/10.16383/j.aas.c200510.
[9]	Park T, Efros A A, Zhang R, et al. Contrastive Learning for Unpaired Image-to-Image Translation[EB/OL]. (2020-08-20)[2021-11-10]. https://arxiv.org/abs/2007.15651.
[10]	Van den Oord A, Li Y, Vinyals O. Representation Learning with Contrastive Predictive Coding[EB/OL]. (2019-01-22)[2021-11-10]. http://export.arxiv.org/abs/1807.03748.
[11]	Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative Adversarial Networks[EB/OL]. (2017-04-03)[2021-11-10]. https://doi.org/10.48550/arXiv.1701.00160.
[12]	He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Las Vegas, NV, USA: IEEE, 2016: 770-778.
[13]	Isola P, Zhu J-Y, Zhou T, et al. Image-to-Image Translation with Conditional Adversarial Networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, 2018.
[14]	Wang T, Isola P. Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere[EB/OL]. (2020-11-10)[2021-12-17]. https://doi.org/10.48550/arXiv.2005.10242.
[15]	Li C, Guo C, Ren W, et al. An Underwater Image Enhancement Benchmark Dataset and Beyond[J]. IEEE Transactions on Image Processing, 2020, 29: 4376-4389. doi: 10.1109/TIP.2019.2955241
[16]	Paszke A, Gross S, Massa F, et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library[EB/OL]. (2019-12-03)[2021-11-07]. https://doi.org/10.48550/arXiv.1912.01703.
[17]	Huynh-Thu Q, Ghanbari M. Scope of Validity of PSNR in Image/Video Quality Assessment[J]. Electronics Letters, IET, 2008, 44(13): 800-801. doi: 10.1049/el:20080522
[18]	Heusel M, Ramsauer H, Unterthiner T, et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium[EB/OL]. (2018-01-12)[2021-11-07]. https://doi.org/10.48550/arXiv.1706.08500.
[19]	Yang M, Sowmya A. An Underwater Color Image Quality Evaluation Metric[J]. IEEE Transactions on Image Processing, 2015, 24(12): 6062-6071. doi: 10.1109/TIP.2015.2491020