基于改进ResUNet分割网络的相位解包裹算法
Phase Unwrapping Algorithm Based on Improved ResUNet Segmentation Network
杨旭彤 1钟平 1靖执义 1叶欣 1郑新立1
作者信息
摘要
二维相位解包裹算法广泛应用于光学计量相关领域中.然而,实际应用场景中的高噪声和相位不连续等复杂环境常常导致传统相位解包裹失败.提出了一种基于深度卷积神经网络(Deep Convolutional Neural Network,DCNN)的方法应用于相位解包裹,该方法将相位解包裹视为一个多像素分类问题,通过引入了改进的ResUNet分割网络来识别类别,分割完成后,将包裹相位图和分割结果相结合即可生成展开相位.在仿真数据集上针对噪声和不连续情况分别与现有的方法进行比较,对于-2 dB噪声水平的包裹相位图,相位展开RMSE为 0.0062,对于相位不连续情况,RMSEm和RMSEsd分别为 0.0017 和 0.0178,远低于ResUNet和其他几种方法.
Abstract
Two-dimensional phase unwrapping algorithms are widely used in optical metrology-related fields.However,complex environments such as high noise and phase discontinuity in practical application scenarios often lead to the failure of traditional phase unwrapping.In this paper,a method based on deep convolutional neural network(DCNN)is proposed for phase unwrapping,which considers phase unwrapping as a multi-pixel classification problem and introduces an improved ResUNet segmentation network to recognize the categories,and after the segmentation is completed,the unwrapped phase map is combined with the segmentation result to generate the unwrapped phase.Once the segmentation is completed,the unwrapped phase can be generated by combining the parcel phase map and the segmentation result.In this paper,we compare with the existing methods on simulation datasets for the noise and discontinuity cases,respectively,and the phase unwrapping RMSE is only 0.0062 for the wrapped phase map with-2 dB noise level,and for the phase discontinuity case,the RMSEm and RMSEsd are 0.0017 and 0.0178,which are much lower than ResUNet and several other methods.
关键词
相移干涉测量/相位解包裹/深度学习/语义分割/不连续相位展开Key words
phase-shift interferometry/phase unwrapping/deep learning/semantic segmentation/discontinuous phase unwrapping引用本文复制引用
基金项目
国家自然科学基金(51975116)
上海市自然科学基金(21ZR1402900)
出版年
2024
NETL
NSTL
万方数据