Xu Min, Zhang Yu, Wan Yingying, Luo Lin, Peng Jianping
School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China.
Micromachines (Basel). 2023 Jan 27;14(2):328. doi: 10.3390/mi14020328.
It is challenging to stably and rapidly achieve accurate absolute phase retrieval for isolated objects with a single-shot pattern in fringe projection profilometry (FPP). In this context, a single-shot multi-frequency absolute phase retrieval (SAPR-DL) method based on deep learning is proposed, which only needs to capture one fringe image to obtain the full-field precise absolute phase. Specifically, a low-frequency deformed fringe image is loaded into the trained one-to-two deep learning framework (DLFT) to predict unit-frequency and high-frequency deformed fringe images. Then, three fringe images with different frequencies are loaded into the trained deep learning phase retrieval framework (DLPR) to calculate the corresponding absolute phase. The experimental results prove that the proposed SAPR-DL method can obtain the three-dimensional (3D) shape measurement of multiple complex objects by collecting a single-shot fringe image, showing great prospects in advancing scientific and engineering applications.
在条纹投影轮廓术（FPP）中，对于具有单次图案的孤立物体，要稳定、快速地实现精确的绝对相位检索具有挑战性。在此背景下，提出了一种基于深度学习的单次多频绝对相位检索（SAPR-DL）方法，该方法只需捕获一幅条纹图像即可获得全场精确的绝对相位。具体而言，将低频变形条纹图像加载到训练好的一对二深度学习框架（DLFT）中，以预测单位频率和高频变形条纹图像。然后，将三幅不同频率的条纹图像加载到训练好的深度学习相位检索框架（DLPR）中，以计算相应的绝对相位。实验结果证明，所提出的SAPR-DL方法通过采集一幅单次条纹图像即可获得多个复杂物体的三维（3D）形状测量结果，在推进科学和工程应用方面显示出巨大的前景。
