扫描透射电子显微镜的深度学习识别：量化与缓解高斯噪声的影响
Deep-Learning Recognition of Scanning Transmission Electron Microscopy: Quantifying and Mitigating the Influence of Gaussian Noises
AI总结：
本研究提出深度学习Mask R-CNN模型识别STEM成像的纳米颗粒，发现高斯噪声决定识别精度，通过应用高斯和非局部均值滤波器有效缓解噪声影响，显著提高识别准确率。
摘要：
扫描透射电子显微镜（STEM）是揭示材料形貌和结构的强大工具，因此引起了科学界和工业界的浓厚兴趣。STEM技术卓越的空间（原子级）和时间（毫秒级）分辨率产生了大量高清数据，从而实现了对材料的高通量和高速分析。另一方面，处理STEM生成的大数据集非常耗时，超出了人工手动处理的能力，迫切需要基于计算机的自动化。在这项工作中，我们提出了一种深度学习掩膜区域神经网络（Mask R-CNN），用于识别STEM成像的纳米颗粒，并生成相关的尺寸分析。该Mask R-CNN模型在不同高斯噪声、颗粒形状和颗粒尺寸的模拟STEM-HAADF结果上进行了测试，结果表明高斯噪声对识别精度具有决定性影响。通过对含噪声的STEM-HAADF结果应用高斯和非局部均值滤波器，噪声的影响得到了很大缓解，识别精度显著提高。这种滤波-识别方法进一步应用于实验STEM-HAADF结果，与传统阈值方法相比，获得了令人满意的精度。本工作开发的基于深度学习的方法在分析STEM-HAADF生成的复杂结构和大数据方面具有巨大潜力。
英文摘要：
Scanning transmission electron microscopy (STEM) is a powerful tool to reveal the morphologies and structures of materials, thereby attracting intensive interests from the scientific and industrial communities. The outstanding spatial (atomic level) and temporal (ms level) resolutions of the STEM techniques generate fruitful amounts of high-definition data, thereby enabling the high-volume and high-speed analysis of materials. On the other hand, processing of the big dataset generated by STEM is time-consuming and beyond the capability of human-based manual work, which urgently calls for computer-based automation. In this work, we present a deep-learning mask region-based neural network (Mask R-CNN) for the recognition of nanoparticles imaged by STEM, as well as generating the associated dimensional analysis. The Mask R-CNN model was tested on simulated STEM-HAADF results with different Gaussian noises, particle shapes and particle sizes, and the results indicated that Gaussian noise has determining influence on the accuracy of recognition. By applying Gaussian and Non-Local Means filters on the noise-containing STEM-HAADF results, the influences of noises are largely mitigated, and recognition accuracy is significantly improved. This filtering-recognition approach was further applied to experimental STEM-HAADF results, which yields satisfying accuracy compared with the traditional threshold methods. The deep-learning-based method developed in this work has great potentials in analysis of the complicated structures and large data generated by STEM-HAADF.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
