Radiation Image Denoising Based on Convolutional Neural Network

被引：0

作者：

Sun Y.-W. ^{[1
,2
]}

Liu H. ^{[3
]}

Cong P. ^{[1
,2
]}

Li L.-T. ^{[1
,2
]}

Xiang X.-C. ^{[1
,2
]}

Guo X.-J. ^{[1
,2
]}

机构：

[1] Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing

[2] Beijing Key Laboratory of Nuclear Detection & Measurement Technology, Beijing

[3] Chinese Academy of Customs Administration, Qinhuangdao

来源：

Yuanzineng Kexue Jishu | / 9卷 / 1678-1682期

关键词：

Convolutional neural network; Image denoising; Radiation image;

D O I：

10.7538/yzk.2017.51.09.1678

中图分类号：

学科分类号：

摘要：

In this paper, a denoising method for statistical noise in detector based on convolutional neural network was proposed. Using a convolutional neural network model with residual blocks, the method training the radiation image samples in the training dataset and the mapping function of image with noise to image without noise was found. The experiment result shows that the method can reduce the statistical noise while maintaining the image details. The method delivers superior performance in both quantitative parameter and visual feeling compared with other traditional methods. © 2017, Editorial Board of Atomic Energy Science and Technology. All right reserved.

引用

页码：1678 / 1682

页数：4

共 7 条

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