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As the gold standard for phase retrieval, phase-shifting algorithm (PS) hasbeen widely used in optical interferometry, fringe projection profilometry,etc. However, capturing multiple fringe patterns in PS limits the algorithm toonly a narrow range of application. To this end, a deep learning (DL) modelbased digital PS algorithm from only a single fringe image is proposed. Bytraining on a simulated dataset of PS fringe patterns, the learnt model,denoted PSNet, can predict fringe patterns with other PS steps when given apattern with the first PS step. Simulation and experiment results demonstratethe PSNet's promising performance on accurate prediction of digital PSpatterns, and robustness to complex scenarios such as surfaces with varyingcurvature and reflectance.

PSNet: a deep learning model based digital phase shifting algorithm from a single fringe image