Laser & Optoelectronics Progress, Volume. 61, Issue 2, 0211024(2024)
Structured Illumination Fringe-Pattern Analysis Based on Digital Twin and Transfer Learning (Invited)
In recent years, deep learning techniques have been widely applied in computational optical three-dimensional imaging. Fringe projection profilometry uses a trained deep neural network to recover high-precision phase information from a single fringe image. However, collecting the training dataset for a neural network expends a considerable amount of time and human resources. To mitigate this problem, we establish a digital twin-fringe projection system that enhances virtual fringe patterns using domain randomization techniques. A U-Net neural network is pretrained using a large number of simulated fringe-pattern images generated through virtual scanning. Next, transfer learning is introduced and the neural network parameters are fine-tuned using a small number of real fringe-pattern images. Targeting fringe analysis applications, this study proposes and analyzes three U-Net neural network fine-tuning schemes: “from left to right” “from top to bottom” “global fine-tuning”. The experimental results demonstrate that fine-tuning the bottleneck network module of the U-Net under the “from top to bottom” strategy optimizes the transfer learning results, largely improving the phase prediction accuracy of the neural network. The proposed method achieves high-precision phase reconstruction results after training the neural network on only 20% of the real data, thus avoiding the need for a large real dataset.
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Ziheng Jin, Ke Xu, Ningyuan Zhang, Xiao Deng, Chao Zuo, Qian Chen, Shijie Feng. Structured Illumination Fringe-Pattern Analysis Based on Digital Twin and Transfer Learning (Invited)[J]. Laser & Optoelectronics Progress, 2024, 61(2): 0211024
Category: Imaging Systems
Received: Nov. 3, 2023
Accepted: Dec. 13, 2023
Published Online: Feb. 6, 2024
The Author Email: Feng Shijie (shijiefeng@njust.edu.cn)