Fringe pattern analysis using deep learning

By Shijie Feng, Qian Chen, Guohua Gu, Tianyang Tao, Liang Zhang, Yan Hu, Wei Yin, Chao Zuo. Advanced Photonics, 2018

415 citations recorded by the source corpus.

In many optical metrology techniques, fringe pattern analysis is the central algorithm for recovering the underlying phase distribution from the recorded fringe patterns. Despite extensive research efforts for decades, how to extract the desired phase information, with the highest possible accuracy, from the minimum number of fringe patterns remains one of the most challenging open problems. Inspired by recent successes of deep learning techniques for computer vision and other applications, we demonstrate for the first time, to our knowledge, that the deep neural networks can be trained to perform fringe analysis, which substantially enhances the accuracy of phase demodulation from a single fringe pattern. The effectiveness of the proposed method is experimentally verified using carrier fringe patterns under the scenario of fringe projection profilometry. Experimental results demonstrate its superior performance, in terms of high accuracy and edge-preserving, over two representative single-frame techniques: Fourier transform profilometry and windowed Fourier transform profilometry.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7bf5b08a15172e373472a66a0b9d1e5934b87f6c

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In Computer Vision and Pattern Recognition

1. Chao Zuo
2. Yixuan Li
3. Kemao Qian
4. Pengfei Fan
5. Wei Yin
6. Jing Han
7. Shijie Feng
8. Qian Chen

Citations in the corpus

• Deep learning in optical metrology: a review (2022, methodology): “Analysis Phase demodulation Feng et al.(50,304) FCN+ ResNet Experiment MSE”