Fringe projection profilometry (FPP) and digital image correlation (DIC) are widely applied in three-dimensional (3D) measurements. The combination of DIC and FPP can effectively overcome their respective shortcomings. However, the speckle on the surface of an object seriously affects the quality and modulation of fringe images captured by cameras, which will lead to non-negligible errors in the measurement results. In this paper, we propose a fringe image extraction method based on deep learning technology, which transforms speckle-embedded fringe images into speckle-free fringe images. The principle of the proposed method, 3D coordinate calculation, and deformation measurements are introduced. Compared with the traditional 3D-DIC method, the experimental results show that this method is effective and precise.

In fringe projection profilometry, high-order harmonics information of distorted fringe will lead to errors in the phase estimation. In order to solve this problem, a point-wise phase estimation method based on a neural network (PWPE-NN) is proposed in this paper. The complex nonlinear mapping relationship between the gray values and the phase under non-sinusoidal distortion is constructed by using the simple neural network model. It establishes a novel implicit expression for phase solution without complicated measurement operations. Compared with the previous method of combining local image information, it can accurately calculate each phase value by point. The comparison results show that the traditional method is with periodic phase errors, while the proposed method can effectively eliminate phase errors caused by non-sinusoidal phase shifting.