量子电子学报 ›› 2022, Vol. 39 ›› Issue (4): 558-565.doi: 10.3969/j.issn.1007-5461.2022.04.010

• 图像与信息处理 • 上一篇    下一篇

金属遮挡条件下光子层析快速重构方法研究

杨一杉1, 梁华为1, 姚瑶2, 陈帅3∗   

  1. ( 1 中国科学院合肥物质科学研究院智能机械研究所, 安徽合肥230031; 2 中北大学仪器与电子学院, 山西太原030000; 3 中国科学院合肥物质科学研究院核能安全技术研究所, 安徽合肥230031 )
  • 收稿日期:2021-01-07 修回日期:2021-03-29 出版日期:2022-07-28 发布日期:2022-07-28
  • 通讯作者: E-mail: shuai.chen@inest.cas.cn E-mail:E-mail: shuai.chen@inest.cas.cn
  • 作者简介:杨一杉( 1992 - ), 安徽蚌埠人, 博士生, 主要从事人工智能方面的研究。E-mail: rossyoung@163.com
  • 基金资助:
    Supported by Strategic Priority Research Program of Chinese Academy of Sciences (中国科学院战略性先导科技专项资助项目, XDA14020503)

Fast reconstruction method of photon tomography under metal shielding condition

YANG Yishan1, LIANG Huawei1, YAO Yao2, CHEN Shuai3∗   

  1. ( 1 Institute of Intelligent Machines, HFIPS, Chinese Academy of Sciences, Hefei 230031, China; 2 School of Instrument and Electronics, North University of China, Taiyuan 030000, China; 3 Institute of Nuclear Energy Safety Technology, HFIPS, Chinese Academy of Sciences, Hefei 230031, China )
  • Received:2021-01-07 Revised:2021-03-29 Published:2022-07-28 Online:2022-07-28

摘要: 传统的光子层析方法难以适用于含有大量重金属遮挡物的对象中。本研究拟从光子层析的视觉特征出 发, 通过针对层析投影开展基于无监督学习的像素预测, 构建了基于金属遮挡物周边信息的编码器, 以生成受遮 挡区域结构。同时, 在生成对抗网络架构的基础上, 将编码器作为生成网络, 并构建判别器用于编码器的训练。 研究表明, 基于像素预测的重构方法能够实现金属结构覆盖区域内光子层析信息的再合成, 并且合成结果能够 准确反映真实的物体的内部细节。这表明基于像素预测的重构方法能够有效降低物体内金属结构对层析结果 的影响。

关键词: 图像处理, 快速重构方法, 像素预测, 无监督学习

Abstract: The traditional photon tomography method cannot be suitable for the objects containing heavy metal shielding. So in this study, based on the visual characteristics of photon tomography, pixel prediction based on unsupervised learning is carried out for the tomographic projection, and an encoder based on the surrounding information of metal shielding is constructed to generate the structure of the sheilded area. At the same time, on the basis of the generative adversarial network (GAN), the encoder is used as the generative network, and the discriminator is built for the training of the encoder. The results show that the reconstruction method based on pixel prediction can reconstruct the photonic tomography information in the area covered by heavy metal shielding, and the reconstruction results can accurately reflect the internal details of real objects, which indicates that the reconstruction method based on pixel prediction can effectively reduce the influence of metal structure in the object on the tomography results.

Key words: image processing, fast reconstruction method, pixel prediction, unsupervised learning

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