Turbid Water Image Enhancement Algorithm Based on Improved CycleGAN

LI Baoqi; HUANG Haining; LIU Jiyuan; LIU Zhengjun; WEI Linzhe

doi:10.11999/JEIT210400

Volume 44 Issue 7

Jul. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(7): 2504-2511

LI Baoqi, HUANG Haining, LIU Jiyuan, LIU Zhengjun, WEI Linzhe. Turbid Water Image Enhancement Algorithm Based on Improved CycleGAN[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2504-2511. doi: 10.11999/JEIT210400

Citation:

LI Baoqi, HUANG Haining, LIU Jiyuan, LIU Zhengjun, WEI Linzhe. Turbid Water Image Enhancement Algorithm Based on Improved CycleGAN[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2504-2511. doi: 10.11999/JEIT210400

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Turbid Water Image Enhancement Algorithm Based on Improved CycleGAN

doi: 10.11999/JEIT210400

1.
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2.
Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing, Chinese Academy of Sciences, Beijing 100190, China

Funds: The National Natural Science Foundation of China (11904386), State Administration of Science, Technology and Industry for National Defence (JCKY2016206A003), Youth Innovation Promotion Association of Chinese Academy of Sciences (2019023)

Received Date: 2021-05-08
Accepted Date: 2022-01-22
Rev Recd Date: 2022-03-01

Available Online: 2022-03-10

Publish Date: 2022-07-25

Abstract

Abstract

In order to solve the problem of poor quality and slow speed in turbid water image enhancement based on Cycle Generative Adversarial Networks (CycleGAN), a scalable, selective and efficient block Bottleneck Selective Dilated Kernel (BSDK) is proposed, and a new generator network BSDKNet is redesigned by stacking BSDK. At the same time, Multi-scale Loss Function (MLF) is proposed to improve the structural similarity of the clear water image and the generated clear water image. On our turbid water image enhancement dataset Turbid and Clear (TC), the classification accuracy of the proposed BM-CycleGAN is 3.27% higher than that of classical CycleGAN. The generator parameters of BM-CycleGAN is 4.15 MB lower than that of CycleGAN, and the time consuming of BM-CycleGAN is 0.107 s less than that of CycleGAN. The experimental results show that BM-CycleGAN is suitable for turbid water image enhancement.

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References(26)

References

[1]	HMUE P M and PUMRIN S. Image enhancement and quality assessment methods in turbid water: A review article[C]. 2019 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia), Bangkok, Thailand, 2019: 59–63.
[2]	HITAM M S, AWALLUDIN E A, YUSSOF W N J H W, et al. Mixture contrast limited adaptive histogram equalization for underwater image enhancement[C]. 2013 International Conference on Computer Applications Technology (ICCAT), Sousse, Tunisia, 2013: 1–5.
[3]	GHANI A S A and ISA N A M. Enhancement of low quality underwater image through integrated global and local contrast correction[J]. Applied Soft Computing, 2015, 37: 332–344. doi: 10.1016/j.asoc.2015.08.033
[4]	LI Chongyi, GUO Jichang, GUO Chunle, et al. A hybrid method for underwater image correction[J]. Pattern Recognition Letters, 2017, 94: 62–67. doi: 10.1016/j.patrec.2017.05.023
[5]	DENG Xiangyu, WANG Huigang, and LIU Xing. Underwater image enhancement based on removing light source color and Dehazing[J]. IEEE Access, 2019, 7: 114297–114309. doi: 10.1109/ACCESS.2019.2936029
[6]	LECUN Y, BENGIO Y, and HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436–444. doi: 10.1038/nature14539
[7]	KWOK R. Deep learning powers a motion-tracking revolution[J]. Nature, 2019, 574(7776): 137–138. doi: 10.1038/d41586-019-02942-5
[8]	WANG Shiqiang. Efficient deep learning[J]. Nature Computational Science, 2021, 1(3): 181–182. doi: 10.1038/s43588-021-00042-x
[9]	KUANG Wenhuan, YUAN Congcong, and ZHANG Jie. Real-time determination of earthquake focal mechanism via deep learning[J]. Nature Communications, 2021, 12(1): 1432. doi: 10.1038/s41467-021-21670-x
[10]	YANG X S. Data Mining and Deep Learning[M]. YANG X S. Nature-Inspired Optimization Algorithms (Second Edition). London : Academic Press, 2021: 239–258.
[11]	GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative Adversarial Networks[C]. Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, Canada, 2014: 2672–2680.
[12]	RADFORD A, METZ L, and CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[C]. 4th International Conference on Learning Representations, San Juan, Puerto Rico, 2016.
[13]	ARJOVSKY M, CHINTALA S, and BOTTOU L. Wasserstein GAN[J]. arXiv Preprint arXiv: 1701.07875, 2017.
[14]	CHEN Xi, DUAN Yan, HOUTHOOFT R, et al. InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets[C]. Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, Barcelona, Spain, 2016: 2172−2180.
[15]	XU Qiantong, HUANG Gao, YUAN Yang, et al. An empirical study on evaluation metrics of generative adversarial networks[J]. arXiv preprint arXiv: 1806.07755, 2018.
[16]	ISOLA P, ZHU Junyan, ZHOU Tinghui, et al. Image−to−image translation with conditional adversarial networks[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 2017: 5967–5976.
[17]	ZHU Junyan, 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, 2017: 2242–2251.
[18]	FABBRI C, ISLAM M J, and SATTAR J. Enhancing underwater imagery using generative adversarial networks[C]. 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2018: 7159–7165.
[19]	XIE Saining, GIRSHICK R, DOLLÁR P, et al. Aggregated residual transformations for deep neural networks[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 2017: 5987–5995.
[20]	SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[C]. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, USA, 2017: 4278–4284.
[21]	LI Xiang, WANG Wenhai, HU Xiaolin, et al. Selective kernel networks[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, 2019: 510–519.
[22]	HUANG Xuejun, WEN Liwu, and DING Jinshan. SAR and optical image registration method based on improved CycleGAN[C]. 2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Xiamen, China, 2019: 1–6.
[23]	李宝奇, 贺昱曜, 强伟, 等. 基于并行附加特征提取网络的SSD地面小目标检测模型[J]. 电子学报, 2020, 48(1): 84–91. doi: 10.3969/j.issn.0372-2112.2020.01.010 LI Baoqi, HE Yuyao, QIANG Wei, et al. SSD with parallel additional feature extraction network for ground small target detection[J]. Acta Electronica Sinica, 2020, 48(1): 84–91. doi: 10.3969/j.issn.0372-2112.2020.01.010
[24]	HOWARD A G, ZHU Menglong, CHEN Bo, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[J]. arXiv Preprint arXiv: 1704.04861, 2017.
[25]	CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834–848. doi: 10.1109/TPAMI.2017.2699184
[26]	QIN Yanjun, LUO Haiyong, ZHAO Fang, et al. NDGCN: Network in network, dilate convolution and graph convolutional networks based transportation mode recognition[J]. IEEE Transactions on Vehicular Technology, 2021, 70(3): 2138–2152. doi: 10.1109/TVT.2021.3060761