通过深度学习从几个光束强度图像中检索的X射线镜头图像错误
Manuel Sanchez Del Rio1, Rafael Celestre1, Juan Reyes-Herrera1
1European Synchrotron Radiation Facility, 71 Avenue des Martyrs, 38000 Grenoble, France.
Journal of synchrotron radiation
|July 23, 2024
概括
研究人员开发了一种新方法,仅使用强度图像来测量X射线镜头偏差. 神经网络准确地预测镜头表面的误差,使光学系统能够进行常规的偏差测量.
科学领域:
- 光学和光子学 在光学和光子学.
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
背景情况:
- 阶段问题是X射线光学中的一个重大挑战,特别是在聚焦同步子束时.
- 精确描述镜头偏差对于高分辨率成像和实验至关重要.
研究的目的:
- 为了解决X射线镜头系统中的相位问题.
- 为了证明只使用强度测量来检索镜头表面错误的可行性.
- 开发一种用于X射线光学系统的常规偏差测量方法.
主要方法:
- 通过随机表面误差的透镜进行X射线束传播的模拟.
- 使用在多个距离传播光束的强度图像.
- 训练一个神经网络,从强度数据中预测镜头错误概况.
主要成果:
- 成功地证明了仅从强度图像中检索镜头表面错误.
- 一个训练有素的神经网络准确地预测了镜头的错误概况,将所有异常计算在内.
- 该方法显示了用于常规测量X射线镜头偏差的可行性.
结论:
- 基于强度的测量是X射线镜头系统中相位问题的可行解决方案.
- 神经网络为表征光学偏差提供了一个有效的工具.
- 这种技术可以应用于X射线镜头和其他光学系统来测量偏差.
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