使用快速里埃转换的卷积神经网络进行中子半阴影图像重建
Jianjun Song1, Jianhua Zheng1, Zhongjing Chen1
1Laser Fusion Reacher Center, China Academic of Engineering Physics, Mianyang, SiChuan 621900, China.
The Review of scientific instruments
|January 24, 2024
概括
一个新的快速里埃变换神经网络 (FFTNN) 重建2D中子辐射图像用于惯性封闭融合 (ICF) 诊断. 这种方法可以提高热点成像的准确性,特别是在高噪音条件下.
科学领域:
- 核聚变科学 核聚变科学
- 等离子体物理学的物理学
- 诊断技术 诊断技术
背景情况:
- 在惯性封闭融合 (ICF) 中的热点不对称性影响了弹裂性能.
- 中子半阴影成像对于诊断ICF中的热点形状至关重要.
研究的目的:
- 开发一种先进的算法,用于从半暗探测器图像中重建二维中子辐射图像.
- 为了提高热点成像在ICF诊断中的准确性和稳定性.
主要方法:
- 开发一个16层神经网络,即快速里埃变换神经网络 (FFTNN),结合FFT,卷积和完全连接的层.
- 由于实验数据的限制,使用现象学热点模型生成训练数据集.
- 与传统的维纳过和Lucy-Richardson算法对比FFTNN的性能.
主要成果:
- 与传统方法相比,FTNN表现出优越的重建性能,特别是在高噪音场景中.
- 评估指标,如平均平方误差和结构相似指数测量,证实了FFTNN的提高准确性.
- 神经网络有效地从半暗数据中重建二维中子辐射图像.
结论:
- 开发的FFTNN为ICF的中子成像重建提供了重大进展.
- 这种方法加强了将中子成像诊断纳入ICF研究的整合.
- FFTNN为分析热点特征提供了更准确,更可靠的方法.
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