Vehicle radiation image restoration based on a generative adversarial network
LENG Zhiying1,2, SUN Yuewen1,2, TONG Jianmin1,2, WANG Zhentao1,2
1. Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China; 2. Beijing Key Laboratory on Nuclear Detection & Measurement Technology, Beijing 100084, China
Abstract: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.
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