Author(s)

Delin Liu¹, Zhen Zhen¹, Yufen Du², Ka Kang², Haonan Zhao², Chuanwei Li², Zhiyong Wang^2*

¹Aviation Key Laboratory of Science and Technology on Advanced Corrosion and Protection for Aviation Material, Beijing Institute of Aeronautical Materials, Beijing, China.
²Department of Mechanics, Tianjin University, Tianjin, China.

Limited by diffraction limit, low spatial resolution is one of the shortcomings of terahertz imaging. Low spatial resolution is also one of the reasons limiting the development of stress measurement using terahertz imaging. In this paper, the full-field stress measurement using Terahertz Time Domain Spectroscopy (THz-TDS) is combined with Super-Resolution Convolutional Neural Network (SRCNN) algorithm to obtain stress fields with high spatial resolution. A modulation model from a plane stress state to a THz-TDS signal is constructed. A large number of simulated sets are obtained to train the SRCNN model. By applying the trained SRCNN model to imaging the numerical and physical stress fields, the improved spatial resolution of stress field calculated from the captured THz-TDS signal is obtained.

THz-TDS, Stress Measurement, Super-Resolution Convolutional Neural Network

Liu, D. , Zhen, Z. , Du, Y. , Kang, K. , Zhao, H. , Li, C. and Wang, Z. (2022) Super-Resolution Stress Imaging for Terahertz-Elastic Based on SRCNN. Optics and Photonics Journal, 12, 253-268. doi: 10.4236/opj.2022.1211019.