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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)
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概括
此摘要是机器生成的。

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.

关键词:
适应性参数优化适应性参数优化功能强度 功能强度 功能强度多操作员增强框架的框架.水下图像增强水下图像增强

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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.