联合国-PUNet用于从单个不均和杂的ESPI阶段模式的阶段解封
概括
这项研究介绍了UN-PUNet,这是一个新的卷积神经网络 (CNN) 模型,用于在电子斑点模式干涉测量 (ESPI) 中进行强大的相解封. 它有效地处理不均的灰度和噪声在单相模式没有预处理.
科学领域:
- 光学和光子学 在光学和光子学.
- 计算机视觉 计算机视觉
- 信号处理 信号处理
背景情况:
- 在电子斑点图案干涉测量 (ESPI) 中的相解封具有挑战性,因为在包装的相图案中灰度和噪声不均.
- 现有的方法通常需要预处理,后处理或参数调整,从而限制了它们的实际应用.
研究的目的:
- 为ESPI中单个,杂和不均的灰度包裹相位模式开发一个强大的和有效的相位解封方法.
- 引入一个新的卷积神经网络 (CNN) 模型,UN-PUNet,旨在应对这些挑战.
主要方法:
- 拟议的UN-PUNet模型具有双分支编码器,多级特征融合,注意模块和跳过连接.
- 创建一个全面的数据集,用于不同不均度,边缘密度和噪声水平的阶段解封.
- 开发和应用混合损失函数 (MS_SSIM + L2) 用于训练联合国-PUNet.
主要成果:
- 联合国-PUNet在模拟和实验ESPI数据上实现了有效和稳健的阶段解封.
- 在定量和定性评估中,该方法在DLPU,VUR-Net和PU-M-Net相比表现优越.
- 废弃性研究证实了拟议的损失功能和注意力模块的有效性.
结论:
- 联合国-PUNet成功地解决了单个,不均和杂的包装相模的相解封问题.
- 该方法保持了结构完整性,消除了斑点噪声,并且在没有预处理或后处理的情况下处理灰度变化.
- 联合国-PUNet在ESPI阶段解封方面取得了重大进展,消除了对参数微调的需求.
相关概念视频
01:22Upsampling
242
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
242
01:15Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)
346
Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
346