A deep learning-based method is proposed to recover the absolute phase value from a single fringe pattern. We propose a deep neural network architecture that includes two subnetworks used for wrapping phase calculation and phase unwrapping, respectively. The training set is generated with the absolute phase obtained by the combination of phase shifting and gray coding. In addition, a reference plane is adopted to provide periodic range information for phase unwrapping. Then according to the output of the well-trained network, a high-quality absolute phase is obtained through only a single fringe pattern of the measured object. Experiments on the test set verify that high accuracy for complex texture objects is acquired using the proposed method, which indicates its potential in high-speed measurement.

中文翻译：

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基于深度学习的单次条纹投影轮廓绝对相位检索

提出了一种基于深度学习的方法来从单个条纹图案中恢复绝对相位值。我们提出了一种深度神经网络架构，其中包括两个分别用于包装相位计算和相位展开的子网络。训练集是用相移和格雷编码相结合得到的绝对相位生成的。此外，采用参考平面为相位展开提供周期性范围信息。然后根据训练好的网络的输出，仅通过被测物体的单个条纹图案就获得了高质量的绝对相位。在测试集上的实验验证了使用所提出的方法可以获得复杂纹理对象的高精度，这表明其在高速测量中的潜力。