Label enhanced and patch based deep learning for phase retrieval from single frame fringe pattern in fringe projection 3D measurement
Label enhanced and patch based deep learning for phase retrieval from single frame fringe pattern in fringe projection 3D measurement
复制标题
标签增强和基于补丁的深度学习，用于条纹投影 3D 测量中单帧条纹图案的相位检索
DOI：
10.1364/oe.27.028929
复制
发表时间：
2019-09-30
期刊：
影响因子：
3.8
通讯作者：
Guo, Qinghua
中科院分区：
文献类型：
--
作者：
Shi, Jiashuo;Zhu, Xinjun;Guo, Qinghua
提出了一种基于标签增强和贴片的深度学习相位检索方法，该方法只需要几个条纹模式作为训练数据集，就可以实现快速准确的相位检索。据我们所知，这是第一次在条纹投影中展示了标签增强和补丁策略用于基于深度学习的相位检索的优势。在该方法中，训练数据集中的增强标记数据用于学习深度神经网络(DNN)的输入条纹和输出增强条纹之间的映射。此外，训练数据被裁剪成小块重叠的小块，以扩大DNN的训练样本。实验结果验证了该方法的有效性，并在动态条纹投影三维测量中得到了应用。(C)OSA开放获取出版协议条款下的2019年美国光学学会
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. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement