论文标题
深度学习相干衍射成像
Deep Learning Coherent Diffractive Imaging
论文作者
论文摘要
我们报告了使用仅使用模拟数据训练的卷积神经网络（CNN）在子角分辨率下进行深度学习相干电子成像的开发。我们通过应用训练有素的CNN来直接从扭曲的六角形硝酸硼，单层石墨烯和AU纳米粒子的质量与由常规Ptychographographic方法重建的曲线质量相当的AU纳米颗粒中直接从电子衍射模式中直接恢复相位图像，从而在实验中证明了这种方法。 CNN和Ptychographic图像之间的傅立叶环相关性表明在0.70和0.55 Angstrom范围内实现了空间分辨率（取决于不同的分辨率标准）。用CNN替换迭代算法并从相干衍射模式进行实时成像的能力预计将在物理和生物科学中找到广泛的应用。
We report the development of deep learning coherent electron diffractive imaging at sub-angstrom resolution using convolutional neural networks (CNNs) trained with only simulated data. We experimentally demonstrate this method by applying the trained CNNs to directly recover the phase images from electron diffraction patterns of twisted hexagonal boron nitride, monolayer graphene and a Au nanoparticle with comparable quality to those reconstructed by a conventional ptychographic method. Fourier ring correlation between the CNN and ptychographic images indicates the achievement of a spatial resolution in the range of 0.70 and 0.55 angstrom (depending on different resolution criteria). The ability to replace iterative algorithms with CNNs and perform real-time imaging from coherent diffraction patterns is expected to find broad applications in the physical and biological sciences.
