基于UCNet的散斑干涉相位解包裹

Phase Unwrapping of Speckle Interferometry Based on UCNet

激光剪切散斑干涉是一种可以测量物理表面位移导数的光学测量技术,广泛应用于无损检测和精密测量等领域。在激光剪切散斑干涉中,相位信息的准确获取对于测量目标的形貌和表面特征至关重要,然而相位信息通常受到噪声和非线性失真等因素的影响。基于此,提出一种基于UCNet的散斑干涉相位解包裹方法。以U-Net为框架在网络中引入平行、对称卷积和多尺度解码器,提高模型理解和利用不同尺度特征信息的能力。同时使用SmoothL1损失函数使模型能够适应不同尺度的任务。运用模拟数据集进行网络训练,对生成网络模型进行模拟测试,并经过实际采集的相位图进行验证,证明了网络的精度与泛化能力。结果表明,UCNet相较于深度学习相位解包裹网络,在结构性相似指数值上提高了1.05倍,能够准确地实现激光剪切散斑干涉相位解包裹。

Laser shear speckle interferometry is an optical measurement technique that can measure the derivative of physical surface displacement.It is widely used in fields such as nondestructive testing and precision measurements.In laser shearing speckle interferometry,accurate acquisition of phase information is crucial for measuring the morphologies and surface features of targets.However,phase information is often affected by factors such as noise and nonlinear distortion.A speckle interferometric phase unwrapping method based on UCNet is proposed to address these factors.In this study,with UNet employed as the framework,parallel symmetric convolutions and multiscale decoders were introduced into the network to improve the model’s ability to understand and utilize feature information at different scales.Simultaneously,the SmoothL1Loss loss function was utilized to enable the model to adapt to tasks at different scales.Datasets for network training were used to simulate and test the generated network model,and actual collected phase maps were used to verify the accuracy and generalization ability of the network.Results show that the structural similarity index of the UCNet network is 1.05 times higher than that of the deep learning phase unwrapping network,and it can accurately achieve laser shear speckle interferometry phase unwrapping.