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Shi, W.Z., Caballero, J., Huszar, F., et al. (2016) Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 27-30 June 2016, 1874-1883.
https://doi.org/10.1109/CVPR.2016.207

has been cited by the following article:

• TITLE: Super-Resolution Stress Imaging for Terahertz-Elastic Based on SRCNN

  AUTHORS: Delin Liu, Zhen Zhen, Yufen Du, Ka Kang, Haonan Zhao, Chuanwei Li, Zhiyong Wang

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

  JOURNAL NAME: Optics and Photonics Journal, Vol.12 No.11, November 28, 2022

  ABSTRACT: 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.