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arXiv 2106.08256cond-mat.mtrl-scieess.IV
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基于四维电子衍射数据的相位反演

Phase retrieval from 4-dimensional electron diffraction datasets

Thomas Friedrich, Chu-Ping Yu, Johan Verbeek, Timothy Pennycook, Sandra Van Aert

摘要:

我们提出了一种用于大规模电子显微镜数据的计算成像模式,该模式使用深度学习方法从噪声/稀疏强度记录中检索复杂波,随后从卷积神经网络(CNN)预测的出射波中重建样本的图像。我们证明了一个适当的前向模型与开放数据框架相结合,可以用来生成大型的合成数据集进行训练。结合使用对应于不同剂量值的泊松噪声来增强数据,我们有效地消除了过拟合问题。CNN的基于U-NET的体系结构适用于手头的任务,并且在保持相对较小的尺寸和快速性能的同时表现良好。该方法的有效性得到证实,通过比较重建完善的方法,使用模拟,以及真正的电子显微镜数据。所提出的方法被证明是有效的,特别是在低剂量范围内,明显的强抑制噪声,良好的空间分辨率,和不同的原子类型的灵敏度,使轻和重元素的同时可视化,使不同的原子物种区分。由于该方法作用于一个非常局部的规模,是比较快的,它有可能被用于近实时重建数据采集过程中。

英文摘要:

We present a computational imaging mode for large scale electron microscopy data, which retrieves a complex wave from noisy/sparse intensity recordings using a deep learning approach and subsequently reconstructs an image of the specimen from the Convolutional Neural Network (CNN) predicted exit waves. We demonstrate that an appropriate forward model in combination with open data frameworks can be used to generate large synthetic datasets for training. In combination with augmenting the data with Poisson noise corresponding to varying dose-values, we effectively eliminate overfitting issues. The U-NET based architecture of the CNN is adapted to the task at hand and performs well while maintaining a relatively small size and fast performance. The validity of the approach is confirmed by comparing the reconstruction to well-established methods using simulated, as well as real electron microscopy data. The proposed method is shown to be effective particularly in the low dose range, evident by strong suppression of noise, good spatial resolution, and sensitivity to different atom types, enabling the simultaneous visualisation of light and heavy elements and making different atomic species distinguishable. Since the method acts on a very local scale and is comparatively fast it bears the potential to be used for near-real-time reconstruction during data acquisition.

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