基于自适应卷积神经网络的三维相干衍射成像重建方法
Adaptive 3D convolutional neural network-based reconstruction method for 3D coherent diffraction imaging
摘要：
提出了一种基于自适应机器学习的相干衍射成像三维晶体重建方法。我们表示的晶体使用球谐函数（SH），并产生相应的合成衍射图案。我们利用3D卷积神经网络（CNN）来学习3D衍射体积和SH之间的映射，该SH描述了生成它们的物理体积的边界。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。
英文摘要：
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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