Field-emission scanning electron microscopy has become a fundamental research tool in the fields of medicine and materials science owing to its effectiveness. However, an inherent contradiction exists between the resolution of field-emission scanning electron microscopy and its field-of-view. To solve this problem, we propose a deep learning-based method for electron microscopy that can simultaneously obtain a large field-of-view and ultrahigh resolution. To solve the super-resolution problem, a deep residual attention network is designed based on residual learning and the attention mechanism, wherein the backbone network employs several residual groups to effectively extract the features; moreover, attention groups append the backbone part to refine and fuse the features. Besides, a high-frequency information retention module is added to acquire high-frequency signals, acting as an effective complement to the deep residual attention network. Owing to the lack of super-resolution datasets for electron microscopy, we created a microscopic butterfly wing dataset. In the experiments, MixUp was also applied to super-resolution problem as a simple and effective data augmentation method, to provide more data to train the model. To evaluate the proposed method, we used standard and self-made datasets with peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as criteria. The results show that the proposed model exhibits a state-of-the-art performance with a PSNR of 25.12 dB and an SSIM of 0.64, and makes field-emission scanning electron microscopy more practical and promising in the field of optical device optimization.

中文翻译：

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基于深度残差注意力网络的深度学习超分辨率电子显微镜

场发射扫描电子显微镜因其有效性已成为医学和材料科学领域的基础研究工具。然而，场发射扫描电子显微镜的分辨率与其视场之间存在固有的矛盾。为了解决这个问题，我们提出了一种基于深度学习的电子显微镜方法，可以同时获得大视场和超高分辨率。针对超分辨率问题，基于残差学习和注意力机制设计了深度残差注意力网络，其中骨干网络采用多个残差组来有效提取特征；此外，注意力组附加了主干部分来细化和融合特征。除了，增加高频信息保留模块，获取高频信号，作为深度残差注意力网络的有效补充。由于缺乏用于电子显微镜的超分辨率数据集，我们创建了一个微观蝴蝶翅膀数据集。在实验中，MixUp 还作为一种简单有效的数据增强方法应用于超分辨率问题，为模型训练提供更多数据。为了评估所提出的方法，我们使用标准和自制数据集，以峰值信噪比（PSNR）和结构相似性（SSIM）为标准。结果表明，所提出的模型表现出最先进的性能，PSNR 为 25.12 dB，SSIM 为 0.64，