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Type: Article × Subject: X-ray imaging ×

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    Deep neural networks for classifying complex features in diffraction images
    (Woodbury, NY : Inst., 2019) Zimmermann, Julian; Langbehn, Bruno; Cucini, Riccardo; Di Fraia, Michele; Finetti, Paola; LaForge, Aaron C.; Nishiyama, Toshiyuki; Ovcharenko, Yevheniy; Piseri, Paolo; Plekan, Oksana; Prince, Kevin C.; Stienkemeier, Frank; Ueda, Kiyoshi; Callegari, Carlo; Möller, Thomas; Rupp, Daniela
    Intense short-wavelength pulses from free-electron lasers and high-harmonic-generation sources enable diffractive imaging of individual nanosized objects with a single x-ray laser shot. The enormous data sets with up to several million diffraction patterns present a severe problem for data analysis because of the high dimensionality of imaging data. Feature recognition and selection is a crucial step to reduce the dimensionality. Usually, custom-made algorithms are developed at a considerable effort to approximate the particular features connected to an individual specimen, but because they face different experimental conditions, these approaches do not generalize well. On the other hand, deep neural networks are the principal instrument for today's revolution in automated image recognition, a development that has not been adapted to its full potential for data analysis in science. We recently published [Langbehn et al., Phys. Rev. Lett. 121, 255301 (2018)] the application of a deep neural network as a feature extractor for wide-angle diffraction images of helium nanodroplets. Here we present the setup, our modifications, and the training process of the deep neural network for diffraction image classification and its systematic bench marking. We find that deep neural networks significantly outperform previous attempts for sorting and classifying complex diffraction patterns and are a significant improvement for the much-needed assistance during postprocessing of large amounts of experimental coherent diffraction imaging data.
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    Achieving diffraction-limited resolution in soft-X-ray Fourier-transform holography
    (Amsterdam : Elsevier Science, 2020) Geilhufe, Jan; Pfau, Bastian; Günther, Christian M.; Schneider, Michael; Eisebitt, Stefan
    The spatial resolution of microscopic images acquired via X-ray Fourier-transform holography is limited by the source size of the reference wave and by the numerical aperture of the detector. We analyze the interplay between both influences and show how they are matched in practice. We further identify, how high spatial frequencies translate to imaging artifacts in holographic reconstructions where mainly the reference beam limits the spatial resolution. As a solution, three methods are introduced based on numerical post-processing of the reconstruction. The methods comprise apodization of the hologram, refocusing via wave propagation, and deconvolution using the transfer function of the imaging system. In particular for the latter two, we demonstrate that image details smaller than the source size of the reference beam can be recovered up to the diffraction limit of the hologram. Our findings motivate the intentional application of a large reference-wave source enhancing the image contrast in applications with low photon numbers such as single-shot experiments at free-electron lasers or imaging at laboratory sources.