论文标题
来自单个传输电子显微照片的退出波函数重建，深度学习
Exit Wavefunction Reconstruction from Single Transmission Electron Micrographs with Deep Learning
论文作者
论文摘要
传统的传输电子显微镜（CTEM）仅记录了图像的强度，而不是相位，而是通过常规透射电子显微镜（CTEM）未能发现的一半波函数信息。在深度学习到光学全息图相恢复的成功应用之后，我们开发了神经网络，以从CTEM强度中恢复阶段，用于包含98340退出波源的新数据集。用CLTEM多层传播模拟波形，从晶体学开放数据库中的12789材料进行模拟。我们的网络可以在约25毫秒内恢复大量物理超参数和材料的224x224波函数，我们证明，随着波形的分布受到限制，性能会提高。深度学习的阶段恢复克服了传统方法的局限性：它是活的，不容易扭曲，不需要显微镜修改或多个图像，并且可以应用于任何成像制度。本文介绍了多种通过深度学习进行CTEM阶段恢复的方法，并旨在建立未来研究以改善的起点。源代码和我们新数据集和预培训模型的链接可在https://github.com/jeffrey-ede/one-shot上找到
Half of wavefunction information is undetected by conventional transmission electron microscopy (CTEM) as only the intensity, and not the phase, of an image is recorded. Following successful applications of deep learning to optical hologram phase recovery, we have developed neural networks to recover phases from CTEM intensities for new datasets containing 98340 exit wavefunctions. Wavefunctions were simulated with clTEM multislice propagation for 12789 materials from the Crystallography Open Database. Our networks can recover 224x224 wavefunctions in ~25 ms for a large range of physical hyperparameters and materials, and we demonstrate that performance improves as the distribution of wavefunctions is restricted. Phase recovery with deep learning overcomes the limitations of traditional methods: it is live, not susceptible to distortions, does not require microscope modification or multiple images, and can be applied to any imaging regime. This paper introduces multiple approaches to CTEM phase recovery with deep learning, and is intended to establish starting points to be improved upon by future research. Source code and links to our new datasets and pre-trained models are available at https://github.com/Jeffrey-Ede/one-shot
