Buakor Khachiwan, Zhang Yuhe, Birnšteinová Šarlota, Bellucci Valerio, Sato Tokushi, Kirkwood Henry, Mancuso Adrian P, Vagovic Patrik, Villanueva-Perez Pablo
Opt Express. 2022 Mar 28;30(7):10633-10644. doi: 10.1364/OE.451914.
X-ray free-electron lasers (XFELs) provide high-brilliance pulses, which offer unique opportunities for coherent X-ray imaging techniques, such as in-line holography. One of the fundamental steps to process in-line holographic data is flat-field correction, which mitigates imaging artifacts and, in turn, enables phase reconstructions. However, conventional flat-field correction approaches cannot correct single XFEL pulses due to the stochastic nature of the self-amplified spontaneous emission (SASE), the mechanism responsible for the high brilliance of XFELs. Here, we demonstrate on simulated and megahertz imaging data, measured at the European XFEL, the possibility of overcoming such a limitation by using two different methods based on principal component analysis and deep learning. These methods retrieve flat-field corrected images from individual frames by separating the sample and flat-field signal contributions; thus, enabling advanced phase-retrieval reconstructions. We anticipate that the proposed methods can be implemented in a real-time processing pipeline, which will enable online data analysis and phase reconstructions of coherent full-field imaging techniques such as in-line holography at XFELs.
X射线自由电子激光器（XFEL）能产生高亮度脉冲，为诸如同轴全息术等相干X射线成像技术提供了独特机遇。处理同轴全息数据的一个基本步骤是平场校正，它可减轻成像伪影，进而实现相位重建。然而，由于自放大自发辐射（SASE）的随机性，传统的平场校正方法无法校正单个XFEL脉冲，SASE是产生XFEL高亮度的机制。在此，我们基于欧洲XFEL测量的模拟数据和兆赫兹成像数据，展示了通过使用基于主成分分析和深度学习的两种不同方法克服这一限制的可能性。这些方法通过分离样品和平场信号贡献，从单个帧中检索平场校正图像；从而实现先进的相位检索重建。我们预计所提出的方法可在实时处理流程中实现，这将使在线数据分析以及诸如XFEL同轴全息术等相干全场成像技术的相位重建成为可能。
