Skeleton extraction and inpainting from poor, broken ESPI fringe with an M-net convolutional neural network

Liu Chenxiu; Tang Chen; Xu Min; Hao Fugui; Lei Zhenkun

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Skeleton extraction and inpainting from poor, broken ESPI fringe with an M-net convolutional neural network

机译：骨架提取和从差，破碎的ESPI边缘与M净卷积神经网络

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Extracting skeletons from fringe patterns is the key to the fringe skeleton method, which is used to extract phase terms in electronic speckle pattern interferometry (ESPI). Because of massive inherent speckle noise, extracting skeletons from poor, broken ESPI fringe patterns is challenging. In this paper, we propose a method based on a modified M-net convolutional neural network for skeleton extraction from poor, broken ESPI fringe patterns. In our method, we pose the problem as a segmentation task. The M-net performs excellent segmentation, and we modify its loss function to suit our task. The broken ESPI fringe patterns and corresponding complete skeleton images are used to train the modified M-net. The trained network can extract and inpaint the skeletons simultaneously. We evaluate the performance of the network on two groups of computer-simulated ESPI fringe patterns and two groups of experimentally obtained ESPI fringe patterns. Two related recent methods, the gradient vector fields based on variational image decomposition and the U-net based method, are compared with our method. The results demonstrate that our method can obtain accurate, complete, and smooth skeletons in all cases, even where fringes are broken. It outperforms the two compared methods quantitatively and qualitatively. (C) 2020 Optical Society of America

机译：从条纹图案中提取骨架是边缘骨架方法的关键，用于提取电子散斑图案干涉法（ESPI）中的相位术语。由于巨大的固有斑点噪声，从差的骨架中提取骨骼，破碎的ESPI边缘图案是具有挑战性的。在本文中，我们提出了一种基于改进的M净卷积神经网络的方法，用于缺口，破碎的ESPI条纹图案的骨架提取。在我们的方法中，我们将问题构成为分段任务。 M-Net执行出色的分段，我们修改其损耗功能以适应我们的任务。破碎的ESPI条纹图案和相应的完整骨架图像用于培训修改的M网络。训练有素的网络可以同时提取和修复骨架。我们评估网络对两组计算机模拟ESPI边缘图案的性能和两组实验获得的ESPI条纹图案。与我们的方法比较了两个相关最近的方法，基于变分图像分解的梯度矢量字段和基于U-Net的方法。结果表明，我们的方法可以在所有情况下获得准确，完整和平滑的骨骼，即使在条纹被打破。定量和定性地优于两个比较的方法。（c）2020美国光学学会

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来源
《Applied optics》 |2020年第17期|共9页
作者
Liu Chenxiu; Tang Chen; Xu Min; Hao Fugui; Lei Zhenkun;
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作者单位

Tianjin Univ Sch Elect &

Informat Engn Tianjin 300072 Peoples R China;

Tianjin Univ Sch Elect &

Informat Engn Tianjin 300072 Peoples R China;

Tianjin Univ Sch Elect &

Informat Engn Tianjin 300072 Peoples R China;

Tianjin Univ Sch Elect &

Informat Engn Tianjin 300072 Peoples R China;

Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China;

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正文语种 eng
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Skeleton extraction and inpainting from poor, broken ESPI fringe with an M-net convolutional neural network

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