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功能增强的X射线成像使用融合神经网络策略与可设计的元表面

Hao Shi^1,2,3, Yuanhe Sun^1,2, Zhaofeng Liang¹

¹Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China.

Nanophotonics (Berlin, Germany)

|December 16, 2024

概括

这项研究结合了X射线成像和神经网络后处理,使用了一种新的光电子卷积自动编码器. 这种融合方法提高了图像质量,并减少了对改进的X射线成像应用程序的数据损失.

科学领域:

物理物理学物理
材料科学材料科学材料科学
计算机视觉计算机视觉

背景情况:

基于闪的X射线成像对于科学和工程中的吸收对比可视化至关重要.
目前的方法依赖于闪器和神经网络后处理的单独改进,导致信息丢失和更高的培训成本.
现有的技术往往需要对特定样本类型进行广泛的数据预收集.

研究的目的:

开发用于X射线成像的综合成像和后处理框架.
克服分开光学传输和电子计算过程的局限性.
为了提高特征特定的图像质量和减少计算开销.

主要方法:

一个可设计的光学卷积元面与闪光器的集成.
开发一个融合光电子卷积式自动编码器网络.
自动编码器直接连接到X射线下降转换过程.

主要成果:

证明了不连贯的X射线图像的特征特定增强.
在没有额外的数据预收集的情况下,实现了对多类样本的适用性.
实验验证显示了增强的纹理和区域特征,信号噪声比提高了多达11.2dB.

结论:

拟议的融合光电子框架有效地整合了X射线成像和神经网络后处理.
这种方法最大限度地减少了光学信息损失,并降低了培训成本.
该方法显示了对推进X射线成像的巨大潜力,特别是在数字识别和生物成像方面.

关键词:

的X射线光学.卷积式自动编码器的自动编码器拒绝使用,拒绝使用.metasurface 地表的表面是什么

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