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Fengxu Guan, Siqi Lu, Haitao Lai and Xue Du
Underwater optical imaging devices are often affected by the complex underwater environment and the characteristics of the water column, which leads to serious degradation and distortion of the images they capture. Deep learning-based underwater image en...
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Tao Jiang, Yize Sun, Hai Huang, Hongde Qin, Xi Chen, Lingyu Li, Zongyu Zhang and Xinyue Han
Autonomous underwater manipulation is very important for the robotic and intelligence operations of oceanic engineering. However, a small target often involves limited features and results in inaccurate visual matching. In order to improve visual measure...
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Chengxi Wu, Yuewei Dai, Liang Shan and Zhiyu Zhu
This paper focuses on developing a data-driven trajectory tracking control approach for autonomous underwater vehicles (AUV) under uncertain external disturbance and time-delay. A novel model-free adaptive predictive control (MFAPC) approach based on a f...
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Erkang Chen, Tian Ye, Qianru Chen, Bin Huang and Yendo Hu
Underwater images often suffer from low contrast, low visibility, and color deviation. In this work, we propose a hybrid underwater enhancement method consisting of addressing an inverse problem with novel Retinex transmission map estimation and adaptive...
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Biao Wang, Haobo Zhang, Yunan Zhu, Banggui Cai and Xiaopeng Guo
Low energy consumption has always been one of the core issues in the routing design of underwater sensor networks. Due to the high cost and difficulty of deployment and replacement of current underwater nodes, many underwater applications require the rou...
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