基于自适应三维卷积神经网络的三维相干衍射成像重建方法

A. Scheinker, R. Pokharel
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引用次数: 27

摘要

我们提出了一种新的基于自适应机器学习的方法,用于从相干衍射成像(CDI)中重建三维(3D)晶体。我们用球面谐波(SH)来表示晶体,并生成相应的合成衍射图。我们利用三维卷积神经网络(CNN)来学习三维衍射体和描述它们产生的物理体边界的SH之间的映射。我们使用3D cnn预测的SH系数作为初始猜测,然后使用自适应模型独立反馈进行微调以提高精度。
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Adaptive 3D convolutional neural network-based reconstruction method for 3D coherent diffraction imaging
We present a novel adaptive machine-learning based approach for reconstructing three-dimensional (3D) crystals from coherent diffraction imaging (CDI). We represent the crystals using spherical harmonics (SH) and generate corresponding synthetic diffraction patterns. We utilize 3D convolutional neural networks (CNN) to learn a mapping between 3D diffraction volumes and the SH which describe the boundary of the physical volumes from which they were generated. We use the 3D CNN-predicted SH coefficients as the initial guesses which are then fine tuned using adaptive model independent feedback for improved accuracy.
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