Vehicle radiation image restoration based on a generative adversarial network

被引：0
|
作者：
Leng Z. [1 ,2 ]
Sun Y. [1 ,2 ]
Tong J. [1 ,2 ]
Wang Z. [1 ,2 ]
机构：
[1] Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing
[2] Beijing Key Laboratory on Nuclear Detection and Measurement Technology, Beijing
来源：
Qinghua Daxue Xuebao/Journal of Tsinghua University | 2022年 / 62卷 / 10期
关键词：
degradation model; generative adversarial network; image restoration; radiation image;
D O I：
10.16511/j.cnki.qhdxxb.2021.26.038
中图分类号：
学科分类号：
摘要：
In vehicle radiation imaging,the size of the gamma ray source,the response time of detector and signal amplification circuits,statistical fluctuations and other factors degrade the image with blurring and noise.A model was developed to predict the image degradation in a radiation imaging system to support a radiation image restoration method based on DeblurGAN.A set of radiation images with simulated blurring was used to train the DeblurGAN model that was then used to restore the images.The results show that this method effectively eliminates blurring and noise in radiation images to improve imaging quality. © 2022 Press of Tsinghua University. All rights reserved.
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页码：1691 / 1696
页数：5
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