研究一种水下视觉增强方法,该方法基于多操作员框架中的自适应参数优化
Zhiyong Yang1,2, Shengze Yang1,2, Yuxuan Fu1,2
1Engineering Research and Design Institute of Agricultural Equipment, Hubei University of Technology, Wuhan 430068, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一个适应性框架,用于增强水下图像,提高清晰度和颜色. 该方法优化了参数,以获得更好的视觉质量和在各种水条件下的特征稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 水下成像技术的研究.
背景情况:
- 水下图像因亮度减弱,结构退化和色彩扭曲而受到影响.
- 传统的增强方法 (例如,UDCP,HE) 由于水的条件变化而不稳定.
研究的目的:
- 提出一个多操作员水下图像增强框架,具有自适应参数优化.
- 解决水下图像中发光量,结构和色彩降解的挑战.
主要方法:
- 使用CLAHE构建了一个协作管道,具有亮点保护,纹理关口USM和和补偿.
- 开发了一个具有统一得分功能的自适应多操作员参数优化策略.
- 根据水的条件动态调整CLAHE剪辑极限,USM增益和高斯尺度.
主要成果:
- 在EUVP和UIEB数据集上实现了卓越的结构清晰度和自然颜色外观.
- 在RUIE数据集上获得了更高质量的指标 (AG,UIQM).
- 与基线相比,在UVE38K数据集上改善了ORB特征计数 (12.5%),初始匹配 (9.3%) 和UIQM (3.9%).
- 验证了增强特征的几何一致性和时间稳定性.
结论:
- 拟议的适应性框架有效地增强了水下图像,优于传统方法.
- 适应性参数优化可确保在不同的水下环境中提供强大的性能.
- 增强功能显示了下游应用的改进的几何一致性和时间稳定性.
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