Label enhanced and patch based deep learning for phase retrieval from single frame fringe pattern in fringe projection 3D measurement.

Jiashuo Shi,Xinjun Zhu,Qinghua Guo,Limei Song,Hongyi Wang

doi:10.1364/oe.27.028929

Jiashuo Shi, Xinjun Zhu + Show 3 more

Open Access

https://doi.org/10.1364/oe.27.028929

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Abstract

We propose a label enhanced and patch based deep learning phase retrieval approach which can achieve fast and accurate phase retrieval using only several fringe patterns as training dataset. To the best of our knowledge, it is the first time that the advantages of the label enhancement and patch strategy for deep learning based phase retrieval are demonstrated in fringe projection. In the proposed method, the enhanced labeled data in training dataset is designed to learn the mapping between the input fringe pattern and the output enhanced fringe part of the deep neural network (DNN). Moreover, the training data is cropped into small overlapped patches to expand the training samples for the DNN. The performance of the proposed approach is verified by experimental projection fringe patterns with applications in dynamic fringe projection 3D measurement.

Highlights

Fringe projection as a non-contact and whole field three dimensional (3D) shape measurement technology with high speed, high resolution, and low cost has been widely employed in diverse fields with biomedical applications, industrial and scientific applications, kinematics applications, and biometric identification applications [1,2,3]
Different from previous work, this method can effectively denoise and enhance the fringe part to improve the accuracy of phase extraction results for objects with edges
The real fringe pattern and the corresponding denoise fringe part are as the input data and output labeled data of the deep neural network (DNN), so that the trained network can predict an enhanced fringe part of the given fringe pattern

Summary

Introduction

Fringe projection as a non-contact and whole field three dimensional (3D) shape measurement technology with high speed, high resolution, and low cost has been widely employed in diverse fields with biomedical applications, industrial and scientific applications, kinematics applications, and biometric identification applications [1,2,3]. The principle of this method is to measure the deformation of projected fringe pattern demodulated by the height of tested object. The former only requires one fringe pattern in single shot, which makes it less interfered by the external environment and is more suitable for 3D measurement of dynamic objects [5,6,7]

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