联合双分支排用于水下立体声深度估计
Jingxin Zhou1, Yeqi Hu1, Yuan Rao1
1College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
这项研究介绍了联合双分支排泄 (JDBD),这是一个新的框架,可以提高水下深度估计. 在具有挑战性的海洋环境中,JDBD提高了精度和视觉质量.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 海洋勘探 海洋勘探
背景情况:
- 水下成像面临着光衰减,散射和扭曲的退化.
- 有限的真实立体声数据阻碍了在海洋环境中准确的深度估计.
研究的目的:
- 为准确的水下深度估计开发一个强大的框架.
- 为了应对水下场景中的图像退化挑战.
主要方法:
- 拟议的联合双分支排泄 (JDBD) 作为双分支深度估计网络的插件.
- 在单眼和立体路径之间的双向精细化中实现了任务意识的denoising.
- 使用自适应白平衡,红色反向通道前置和联合双边过来进行图像校正.
主要成果:
- 在现实世界和合成水下数据集上,JDBD实现了高深度估计准确性和视觉准确性.
- 在各种水下条件下表现出强大的性能.
- 与现有方法相比,显著提高视觉质量和深度准确性.
结论:
- 联合开发开发开发框架有效地提高了水下深度估计.
- 提出的方法成功地减轻了图像退化挑战.
- JDBD为各种水下应用提供了可适应的解决方案.
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