适应式多通道除尘,以提高地下煤矿图像的可见性
Yingbo Fan1, Shanjun Mao1, Mei Li1
1Institute of Remote Sensing and Geographic Information Systems, Peking University, Beijing, China.
PloS one
|November 5, 2025
概括
本研究介绍了一种自适应的多通道除烟算法,用于在地下煤矿中获得更清晰的图像. 该方法提高了关键安全监控应用的可见性和实时性能.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 在需要清晰视觉效果的应用中,图像消光至关重要.
- 现有的方法在具有挑战性的地下煤矿条件下表现不佳 (暗光,大气干扰).
研究的目的:
- 开发一种适应性的多通道除气算法,专门用于地下煤矿环境.
- 为了提高图像清晰度,稳定性和安全监控的实时性能.
主要方法:
- 采用了改进的颜色衰减先前与纹理和HSV照明不变性用于雾检测.
- 使用区域分离 (清晰与雾) 采用多尺度金字塔和引导过用于传导率估计.
- 实施了参数重复使用机制,以实现高效的视频脱.
主要成果:
- 与现有方法相比,拟议的算法显示出更高的除雾效率.
- 在计算效率和稳定性方面取得了显著的改进.
- 在具有挑战性的煤矿数据集中成功提高了图像清晰度.
结论:
- 适应式多通道除尘算法对地下煤矿图像增强是有效和高效的.
- 该方法适用于实时应用,如安全监控.
- 为低光,高干扰成像场景提供了强大的解决方案.
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