@inproceedings{61e4b3bf063f4152a1991801bf725078,
title = "Modified Generative Adversarial Network for Super-Resolution of Terahertz Image",
abstract = "Terahertz (THz) images have low spatial resolution, blurring contour features and high background noise owing to the limitation of terahertz (THz) wavelengths and the THz imaging systems. We have proposed a modified Generative Adversarial Network (GAN) for super-resolution (SR) purpose. To fit the THz images, we design a kind of image degradation model to generate low-resolution images with Gaussian blur and white Gaussian noise. We establish a dataset of damage images in the field of non-destructive testing (NDT) for training and testing. The experimental results on THz images demonstrate that the improved GAN model can improve the quality of THz images effectively. Our method can be beneficial to improve the accuracy of THz NDT with low resolution.",
keywords = "THz image, deep learning, degradation model, super-resolution",
author = "Zhen Zhang and Liuyang Zhang and Xuefeng Chen and Yafei Xu",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 1st International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 ; Conference date: 15-10-2020 Through 17-10-2020",
year = "2020",
month = oct,
day = "15",
doi = "10.1109/ICSMD50554.2020.9261734",
language = "英语",
series = "International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "602--605",
booktitle = "International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings",
}