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水下目标检测利用两极化图像融合算法 基于无监督学习和注意力机制
Haoyuan Cheng1, Deqing Zhang1, Jinchi Zhu1
1College of Engineering, Ocean University of China, Qingdao 266100, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
这项研究引入了一种深度学习方法,用于融合水下极化和强度图像,显著提高图像清晰度和细节,以改善水下视觉应用. 该技术可以提高图像质量,而无需手动调整参数.
科学领域:
- 计算机视觉 计算机视觉
- 光学工程是指光学工程.
- 海洋技术 海洋技术
背景情况:
- 水下图像的亮度低,模糊,以及由于光吸收和散射而失去的细节.
- 传统的强度摄像头在水生环境中提供有限的信息.
研究的目的:
- 开发一种基于深度学习的方法,用于融合水下极化和强度图像.
- 提高水下图像的质量和细节,以进行增强的视觉分析.
主要方法:
- 一个深度聚变网络被设计用于合并极化和强度图像.
- 一个由注意力机制指导的无监督学习框架被用于图像融合.
- 创建了一个定制的水下极化图像数据集,并为训练增强了它.
主要成果:
- 与强度图像相比,合并的水下图像显示出明显改善的细节,信息率增加了24.48%,标准偏差增加了139%.
- 该方法在图像处理质量方面优于其他基于融合的方法.
- 改进的U-net网络使得即使在水条件下,目标细分也可行.
结论:
- 拟议的深度融合方法有效地提高了水下图像质量.
- 该技术提供了强度,自适应性和更快的操作速度,适用于海洋检测和水下目标识别.
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