Dual-frequency phase unwrapping based on deep learning driven by simulation dataset

Abstract

In fringe projection profilometry (FPP), hierarchical temporal phase unwrapping is used to reliably eliminate phase ambiguity in complex scenarios, in which triple-frequency hierarchical temporal phase unwrapping (THPU) has excellent accuracy but lower measurement speed, while dual-frequency hierarchical temporal phase unwrapping (DHPU) requires only two wrapped phases, but it is fragile to noise contamination. Some researchers have shown that deep learning techniques can be used to overcome this dilemma, but the high dataset building cost makes it difficult to apply them rapidly. In this paper, a novel simulation dataset generation method for FPP is proposed, which requires only a set of pre-collected fringe sequences on the reference plane along with some historical masks to generate a dataset, which is then used to train a neural network. The appropriately trained neural network is able to perform dual-frequency phase unwrapping in actual scenarios based on the principle of regression and segmentation respectively, and both of them show accuracy comparable to THPU, but segmentation is slightly better than regression. We verify its effectiveness through comparative experiments and demonstrate its robustness with different degrees of noise contamination. We believe that this paper can provide potential and beneficial ideas for simulation dataset driven deep learning techniques for optimizing various processes in the field of optical measurements.