Absolute phase retrieval for a single-shot fringe projection profilometry based on deep learning
Wenjian Li, Jian Yu, Shaoyan Gai, Feipeng Da
Abstract
A deep learning-based method is proposed to recover the absolute phase value from a single fringe pattern. We propose a deep neural network architecture that includes two subnetworks used for wrapping phase calculation and phase unwrapping, respectively. The training set is generated with the absolute phase obtained by the combination of phase shifting and gray coding. In addition, a reference plane is adopted to provide periodic range information for phase unwrapping. Then according to the output of the well-trained network, a high-quality absolute phase is obtained through only a single fringe pattern of the measured object. Experiments on the test set verify that high accuracy for complex texture objects is acquired using the proposed method, which indicates its potential in high-speed measurement.