多补丁层次传输通道 图像脱雾网络 基于双重注意力级别特征融合
1College of Engineering, Sichuan Normal University, Chengdu 610101, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了一种双重注意力级别特征融合多补丁层次网络 (DAMPHN),用于在雾中获得更清晰的无人机图像. 新方法显著提高了基础设施检查的图像质量和处理速度.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 无人机对山区输电线路的检查受到不均的雾的阻碍,掩盖了关键基础设施.
- 现有的方法在功能融合方面扎,导致低于最佳的防雾性能.
研究的目的:
- 为在雾条件下运行的无人机开发一个改进的单一图像除雾网络.
- 提高基础设施检查期间捕获的视觉数据的清晰度和准确性.
主要方法:
- 提出了一种双重注意力级别的功能融合多补丁层次网络 (DAMPHN).
- 引入了UAV-HAZE数据集,使用深度信息和3D柏林噪声模拟非均的雾.
- 评估DAMPHN与现有的脱雾方法和以前的网络 (FDMPHN) 相比.
主要成果:
- 与FDMPHN相比,DAMPHN证明了峰值信号与噪声比率 (PSNR) 和结构相似度指数 (SSIM) 的改进.
- 与FDMPHN相比,实现了0.3dB (PSNR) 和0.011 (SSIM) 的平均增加.
- 与其他三种脱雾方法相比,它表现出优异的性能,平均PSNR和SSIM增幅分别为1.75dB和0.022.
- 与之前的模型相比,平均处理时间 (APT) 减少了11%.
结论:
- 在无人机检查相关的不均的雾条件下,DAMPHN有效地提高了图像质量.
- 拟议的网络提供基于无参考和全参考评估指数的竞争性脱雾结果.
- 新的UAV-HAZE数据集促进了在恶劣天气下基于UAV的功率评估研究.
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