Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi456-8587, Japan.
Technology Innovation Division, Panasonic Corporation, 3-1-1 Yagumo-Nakamachi, Moriguchi, Osaka570-8501, Japan.
Microsc Microanal. 2020 Jun;26(3):429-438. doi: 10.1017/S1431927620001452.
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.
我们通过使用模拟全息图进行学习和验证过程，将机器学习方法稀疏编码的适用性扩展到低剂量电子全息术。这些带有散粒噪声的全息图被用来生成一个模型，或字典，其中包含代表干涉条纹的基本特征。该字典应用于具有不同信噪比 (SNR) 的其他模拟全息图的稀疏表示。结果表明，该方法可以成功去除 SNR 极低为 0.10 的全息图中的噪声，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到的字典可以应用于不同曝光时间记录的 p-n 结样品的实验全息图的去噪。结果表明，经过模拟训练的稀疏编码适用于广泛的成像条件，特别是用于观察对电子束敏感的材料。
