Deep learning-based temporal phase unwrapping method for fringe projection profilometry

A deep learning-based temporal phase unwrapping method for fringe projection profilometry is characterized in that the specific steps are as follows:

• step one, four sets of three-step phase-shifting fringe patterns with different frequencies (including 1, 8, 32, and 64) are projected to tested objects;
• the projected fringe patterns are captured by a camera simultaneously to acquire four sets of three-step phase-shifting fringe images;
• step two, the three-step phase-shifting fringe images acquired by the camera are processed to obtain a wrapped phase map using a three-step phase-shifting algorithm;
• step three, a multi-frequency temporal phase unwrapping (MF-TPU) algorithm is used to unwrap four wrapped phase maps successively to obtain a fringe order map and an absolute phase map of a high-frequency phase with 64 periods;
• step four, a residual convolutional neural network is built to implement phase unwrapping;
• steps one to three are repeatedly performed to obtain multiple sets of data, which are divided into a training dataset, a validation dataset, and a test dataset;
• the training dataset is used to train the residual convolutional neural network;
• the validation dataset is used to verify the performance of the trained network;
• step five, the residual convolutional neural network after training and validation makes predictions on the test dataset to realize a precision evaluation of the network and output the fringe order map of the high-frequency phase with 64 periods.