基于深度学习的电子散斑干涉条纹图相位恢复

Phase Recovery of Electronic Speckle Interferometric Fringe Pattern Using Deep Learning

针对单幅电子散斑干涉条纹图的相位恢复问题,以U-Net为基础网络,融合子像素卷积模块和结构化特征增强模块,提出了USS-Net,实现对单幅条纹图端到端的相位恢复。首先改进上采样方式,采用子像素卷积使网络能学习到更多的条纹细节信息,同时降低反卷积零值填充对梯度计算的影响。其次在编码部分改进特征融合方式,采用结构化特征增强模块,充分融合不同尺度的特征信息,解决条纹疏密程度不均导致特征提取不佳的问题,进而提升对单个像素点的分割准确性。建立了ESPI条纹-相位仿真和实验数据集,对USS-Net模型进行测试与分析,验证所提方法的有效性。所提方法克服了传统相位恢复方法过程繁琐、容易受噪声干扰等缺点,有效提高了单幅条纹图相位恢复的准确率。

To solve the problem of the phase recovery of a single electronic speckle interferometric fringe pattern,we propose a USS-Net,which combines a subpixel convolution module and a structured feature enhancement module to realize end-to-end phase recovery of a single fringe pattern using U-Net as the basic network.First,the upsampling method of U-Net is improved,and the subpixel convolution module is used to make the proposed network learn more fringe details while reducing the influence of deconvolution zero filling on gradient calculation.Second,in the coding part,the feature fusion method of U-Net is improved,and the structured feature enhancement module is used to fully integrate feature information with different scales.Hence,the proposed method can solve the problem of poor feature extraction caused by uneven fringe density and increase the segmentation accuracy for a single pixel point.The electronic speckle pattern interferometry(ESPI)fringe-phase simulation and experimental datasets are established,and the USS-Net model is tested and analyzed to verify the effectiveness of the proposed method.The proposed method overcomes the shortcomings of traditional phase recovery methods,such as cumbersome processes and high susceptibility to noise disturbance,and effectively increases the accuracy of phase recovery of a single fringe pattern.