Phase unwrapping is a general problem in many measurement fields, and plenty mature methods have been put up with. Among them, Laplacian operator combined Fast Fourier-based phase unwrapping method is widely used because of its high speed and relative robustness. However, in some applications such as structured-light 3D reconstruction, there always appear shadows of object in the object-edge regions. These shadows and discontinuity regions might cause big errors in unwrapping results, which will be even more serious for the Fast Fourier-based method. Therefore, in this paper, we analyzed the problem theoretically and present a modified algorithm based on the reference-phase mask. With a designed phase mask, this practical algorithm can remove errors effectively and reach a more precise result. The phase mask will be produced automatically based on the edge detection of the problematic regions combined with an empirical mode decomposition algorithm. The whole procedure will be complemented without manual intervention. Comparison experiments show that our method has great improvement than the original methods in the aspects of accuracy speed, and robustness. This enables the Laplacian based unwrapping algorithm to have compatibility with more optical, physical, medical and engineering occasions.
In the field of precise 3D reconstruction, fringe pattern profilometry (FPP) is always regarded as the preferred method for it provides relatively higher accuracy. However, the phase acquisition process generally requires a sequence of images with different phase shift, which is rather time-consuming. Thus the application scenario of FPP is greatly limited and this has long been a bottleneck in practice. Although single-frame based phase retrieval algorithms like Fourier transform profilometry (FTP) has been proposed and extensively studied, they still suffer from relatively unbearable loss of accuracy. In response to this problem, we take advantage of the deep learning techniques and present a deep-learning based phase acquisition system in which the phase can be acquired by a single frame of fringe pattern image. The network is constructed according to the procedure of phase retrieval, which is trained by thousands of fringe pattern images with the phase data being known in advance. And it can predict more preciously the phase of a new fringe pattern map. Experiments illustrate the effect of our method which will be promising for practical use.