Bi-frequency temporal phase unwrapping using deep learning

Abstract

In fringe projection profilometry (FPP), multi-frequency phase unwrapping, as a classical algorithm for temporal phase unwrapping (TPU), can eliminate the phase ambiguities and obtain the unwrapped phase with the aid of additional wrapped phase maps with different fringe periods. However, based on the principle of multi-frequency phase unwrapping, it needs multiple groups of fringe patterns with different fringe periods to eliminate the phase ambiguities of the wrapped phase with high-frequency, which is not suitable for high-speed 3D measurement. If two frequency fringe patterns are only projected, the reliability of multi-frequency phase unwrapping will be decreased significantly. Inspired by deep learning techniques, in this study, we demonstrate that the deep neural networks can learn to perform temporal phase unwrapping after appropriate training, which substantially improves the reliability of phase unwrapping compared with the traditional multi-frequency TPU approach even when high-frequency fringe patterns are used. In our experiment, a challenging problem in TPU is that the unwrapped phase of 64-period fringe patterns cannot be directly unwrapped by only using a single-frequency phase, but it can be easily resolved by our method. Experimental results demonstrate the temporal phase unwrapping method using deep learning provides the best unwrapping reliability to realize the absolute 3D measurement for objects with complex surfaces.