Moire<acute accent> fringe analysis across diverse carrier frequencies by deep learning

Moire<acute accent> tomography stands as a potent technique for capturing three-dimensional flow fields, and its precision and accuracy hinge upon the efficiency of moire<acute accent> fringe analysis. In this paper, a deep learning moire<acute accent> fringe analysis (DLMFA) method is proposed, and the wrapped phase information could be predicted and analyzed by training moire<acute accent> fringe datasets across various carrier frequencies. The methodology involves acquiring eight sets of moire<acute accent> fringes with distinct carrier frequencies through a moire<acute accent> tomography system. Subsequently, the real and imaginary components of the first-order spectrum of moire<acute accent> fringes were extracted using Fourier analysis, forming the training datasets for the deep learning model. The trained deep learning model can accurately predict the wrapped phase information corresponding to the moire<acute accent> fringe's carrier frequency. The results show that the convergence rate of training loss and validation loss of deep learning model is gradually faster, the prediction loss is gradually reduced, and the structural similarity is also weaker with the increase of carrier frequency. This indicates the impact of carrier frequency on the model's predictive accuracy robustness against interference, including occlusion and noise. The proposed approach exhibits high accuracy and robustness in moire<acute accent> fringe analysis, demonstrating applicability across diverse carrier frequencies for flow fields measurement. This study introduces a fresh perspective and solution for the intelligent advancement of moire<acute accent> tomography.