We broaden the applicability of sparse coding, a machine learning method, to low-dose electron holography by using simulated holograms for learning and validation processes. The holograms, with shot noise, are prepared to generate a model, or a dictionary, that includes basic features representing interference fringes. The dictionary is applied to sparse representations of other simulated holograms with various signal-to-noise ratios (SNRs). Results demonstrate that this approach successfully removes noise for holograms with an extremely small SNR of 0.10, and that the denoised holograms provide the accurate phase distribution. Furthermore, this study demonstrates that the dictionary learned from the simulated holograms can be applied to denoising of experimental holograms of a p–n junction specimen recorded with different exposure times. The results indicate that the simulation-trained sparse coding is suitable for use over a wide range of imaging conditions, in particular for observing electron beam-sensitive materials.

中文翻译：

---

用于低剂量电子全息术中高精度相位成像的模拟训练稀疏编码

我们通过使用模拟全息图进行学习和验证过程，将稀疏编码（一种机器学习方法）的适用性扩大到低剂量电子全息术。带有散粒噪声的全息图可以生成模型或字典，其中包括表示干涉条纹的基本特征。该词典适用于具有各种信噪比 (SNR) 的其他模拟全息图的稀疏表示。结果表明，该方法成功地去除了 SNR 为 0.10 的全息图的噪声，并且去噪后的全息图提供了准确的相位分布。此外，本研究表明，从模拟全息图中学习的字典可以应用于对不同曝光时间记录的 ap-n 结样品的实验全息图进行去噪。