The breadth, complexity, and volume of data generated by materials characterization using various forms of microscopy has expanded significantly. Combined with increases in computing power, this has led to increased application of techniques from informatics and data science to materials microscopy data, both to improve the data quality and improve the materials information extracted from the data. This review covers recent advances in data science applied to materials microscopy, including problems such as denoising, drift and distortion correction, spectral unmixing, and the use of simulated experiments to derive information about materials from microscopy data. Techniques covered include non-local patch-based methods, component analysis, clustering, optimization, and compressed sensing. Examples illustrate the need to combine several informatics approaches to solve problems and showcase recent advances in materials microscopy made possible by informatics.

使用各种形式的显微镜通过材料表征生成的数据的广度，复杂性和数据量已大大扩展。结合计算能力的提高，这导致从信息学和数据科学到材料显微数据的技术应用越来越广泛，既提高了数据质量，又改善了从数据中提取的材料信息。这篇综述涵盖了应用于材料显微镜的数据科学的最新进展，包括降噪，漂移和失真校正，光谱解混以及使用模拟实验从显微镜数据中获取有关材料信息的问题。涵盖的技术包括基于非本地补丁的方法，组件分析，聚类，优化和压缩感测。