| 基于逆卷积神经网络的等离子体辐射分布断层重建方法 |
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| 引用本文： | 王朝辉,肖建元,秦 宏.基于逆卷积神经网络的等离子体辐射分布断层重建方法[J].核聚变与等离子体物理,2021,41(3):207-214. |
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| 作者姓名： | 王朝辉 肖建元 秦 宏 |
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| 作者单位： | 中国科学技术大学物理学院,合肥 230026 |
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| 基金项目： | 国家自然科学基金(11775219，11575186，11805273)；中国博士后科学基金资助项目(2017LH002) |
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| 摘 要： | 发展了一种基于逆卷积神经网络的图像级重建方法用于聚变等离子体辐射分布的断层反演。通过引 入结构相似度(SSIM)作为损失函数，该方法在模拟数据实验中表现出了较好的重建效果。模拟实验结果表明，在 弦积分信号噪声强度为10%、15%及20%时，该方法的重建结果依然具有良好的精确度和鲁棒性。
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| 关 键 词： | 等离子体辐射分布断层重建 逆卷积神经网络 弦积分诊断系统 图像重建 |
| 收稿时间： | 2020-02-15 |
Plasma radiation distribution tomography based on deconvolutional neural networks |
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| WANG Zhao-hui,XIAO Jian-yuan,QIN Hong.Plasma radiation distribution tomography based on deconvolutional neural networks[J].Nuclear Fusion and Plasma Physics,2021,41(3):207-214. |
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| Authors: | WANG Zhao-hui XIAO Jian-yuan QIN Hong |
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| Institution: | (University of Science and Technology of China Department Physics, Hefei 230026) |
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| Abstract: | A deconvolutional neural network (deCNN) which allows to implement a pixel-wise emission distribution reconstruction is developed for plasma radiation distribution tomographic reconstruction. By adopting the structural similarity (SSIM) as the loss function, the method reveals better reconstruction accuracy in phantom data experiments. The simulated experiments results show that this method could still achieve an admissible reconstruction precision and is noise robustness when the line-integrated noise magnitude is 10%, 15% and 20%. |
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| Keywords: | Plasma radiation distribution tomography Deconvolutional neural networks Line-integrated diagnostic system Image reconstruction |
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