论文标题
3D相干衍射成像的自适应3D卷积神经网络的重建方法
Adaptive 3D convolutional neural network-based reconstruction method for 3D coherent diffraction imaging
论文作者
论文摘要
我们提出了一种新型的基于机器学习方法，用于从相干衍射成像（CDI）重建三维（3D）晶体。我们使用球形谐波（SH）表示晶体，并生成相应的合成衍射模式。我们利用3D卷积神经网络（CNN）学习3D衍射体积和SH描述了产生它们的物理体积的边界的SH。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。
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.
