深度引导双边电网特征融合网络用于除气
Xinyu Li1, Zhi Qiao2, Gang Wan1
1Hubei Technology Innovation Center for Smart Hydropower, Wuhan 430019, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
这项研究引入了一种新的深度学习网络,用于图像消除雾,有效地消除雾,同时保留关键图像细节. 该方法通过整合深度和边缘信息,在恶劣的天气条件下提高可见性.
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:
- 恶劣的雾天气会降低图像质量,降低对比度和可见性.
- 传统的除烟方法在复杂的现实环境中扎.
- 现有的深度学习方法往往无法充分利用深度和边缘信息,导致文物.
研究的目的:
- 提出一个新的深度引导双边电网功能融合排气网络.
- 为了解决当前单图像除尘技术的局限性.
- 为了改善雾清除,同时保持图像细节和边缘清晰度.
主要方法:
- 开发了一个深度引导的双边电网功能融合除网络.
- 提取了深度信息,并用于指导双边网格特征采样.
- 特征重建和最终图像估计涉及卷积层和残余连接.
主要成果:
- 拟议的网络在实验中有效地从图像中去除了雾.
- 与现有方法相比,该方法在公共数据集上表现出优异的性能.
- 图像细节和边缘信息在脱气后被成功保存.
结论:
- 深度引导的双边网格特征融合网络为单图像脱提供了有效的解决方案.
- 这种方法在雾条件下显著提高图像质量.
- 该方法对实际应用有希望,需要在恶劣天气下清晰的图像.
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