概括
这项研究介绍了LFE-Net,这是一个新的网络,用于增强边缘投射特征测量 (FPP) 中的低光边缘模式. 该方法显著提高了3D测量的图像质量,超过了现有的技术.
科学领域:
- 光学计量学 在光学计量学
- 计算机视觉 计算机视觉
- 图像处理 图像处理
背景情况:
- 边缘投影分析 (FPP) 对于3D测量至关重要.
- 低光条件降低了边缘图案的质量,阻碍了准确性.
- 现有的增强方法在低光 FPP 图像方面遇到了困难.
研究的目的:
- 开发一个有效的深度学习网络,以增强低光边缘模式.
- 在不利的照明下提高FPP的稳定性和准确性.
- 介绍一个计算高效的解决方案,用于低光图像增强.
主要方法:
- 建议使用RGB和灰度边框图案的LFE-Net架构.
- 集成的波形转换预处理 (WTP) 用于丰富的特征提取.
- 使用的残余卷积变压器并行 (RCTP) 提供上下文信息和灵活性.
主要成果:
- 与MSRCR,MIRNet和HWMNet相比,LFE-Net在增强低光边缘模式方面表现出卓越的表现.
- 实现了显著更快的计算时间:MIRNet的8.66%和HWMNet.Net的40.49%.
- 实验结果验证了拟议网络的有效性和效率.
结论:
- 在FPP中,LFE-Net提供了一个强大的解决方案,用于在低光下增强边缘图案.
- 该网络提供了高质量的结果,并提高了计算效率.
- 这项工作促进了FPP在具有挑战性的照明环境中的应用.
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