A Novel Underwater Image Synthesis Method Based on a Pixel-level Self-Supervised Training Strategy | |
Zhiheng Wu2,3; Zhengxing Wu2,3; Yue Lu2,3; Jian Wang2,3; Junzhi Yu1,3 | |
2021-07 | |
会议日期 | 2021-7 |
会议地点 | Xining, China |
英文摘要 | With the rapid development of deep neural networks, underwater vision plays an increasingly important role in the underwater robotic operation. However, the scarce underwater datasets greatly limit the performance of deep learning on underwater visual tasks, further hindering the applications of underwater operation. To solve this problem, we propose an underwater image synthesis method, which can directly convert the natural light image into the synthetic underwater image end-to-end. Particularly, a pixel-level self-supervised training strategy is designed to maximize the structural similarity between the synthesized and real images, through training the real underwater images. Finally, extensive experiments are carried out, and the obtained results demonstrate the effectiveness and superiority of our methods by quantitative and qualitative comparisons. The proposed underwater image synthesis method offers a valuable sight for underwater vision and manipulating control. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/52267] |
专题 | 复杂系统管理与控制国家重点实验室_水下机器人 |
通讯作者 | Zhengxing Wu |
作者单位 | 1.Peking University 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Zhiheng Wu,Zhengxing Wu,Yue Lu,et al. A Novel Underwater Image Synthesis Method Based on a Pixel-level Self-Supervised Training Strategy[C]. 见:. Xining, China. 2021-7. |
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