Scanning electron microscopy (SEM) plays an important role in the intuitive understanding of microstructures because it can provide ultrahigh magnification. Tens or hundreds of images are regularly generated and saved during a typical microscopy imaging process. Given the subjectivity of a microscopist's focusing operation, blurriness is an important distortion that debases the quality of micrographs. The selection of high-quality micrographs using subjective methods is expensive and time-consuming. This study proposes a new no-reference quality assessment method for evaluating the blurriness of SEM micrographs. The human visual system is more sensitive to the distortions of cartoon components than to those of redundant textured components according to the Gestalt perception psychology and the entropy masking property. Micrographs are initially decomposed into cartoon and textured components. Then, the spectral and spatial sharpness maps of the cartoon components are extracted. One metric is calculated by combining the spatial and spectral sharpness maps of the cartoon components. The other metric is calculated on the basis of the edge of the maximum local variation map of the cartoon components. Finally, the two metrics are combined as the final metric. The objective scores generated using this method exhibit high correlation and consistency with the subjective scores.

中文翻译：

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多纹理SEM显微照片模糊度的无参考质量评估方法

扫描电子显微镜 (SEM) 在直观了解微观结构方面发挥着重要作用，因为它可以提供超高放大倍率。在典型的显微成像过程中，会定期生成和保存数十或数百张图像。鉴于显微镜师对焦操作的主观性，模糊是降低显微照片质量的重要失真。使用主观方法选择高质量的显微照片既昂贵又耗时。本研究提出了一种新的无参考质量评估方法，用于评估 SEM 显微照片的模糊度。根据格式塔感知心理学和熵掩蔽特性，人类视觉系统对卡通组件的失真比对冗余纹理组件的失真更敏感。显微照片最初被分解为卡通和纹理组件。然后，提取卡通成分的光谱和空间锐度图。一种度量是通过组合卡通组件的空间和光谱锐度图来计算的。另一个度量是基于卡通组件的最大局部变化图的边缘计算的。最后，将这两个指标组合为最终指标。使用这种方法生成的客观分数与主观分数表现出高度的相关性和一致性。另一个度量是基于卡通组件的最大局部变化图的边缘计算的。最后，将这两个指标组合为最终指标。使用这种方法生成的客观分数与主观分数表现出高度的相关性和一致性。另一个度量是基于卡通组件的最大局部变化图的边缘计算的。最后，将这两个指标组合为最终指标。使用这种方法生成的客观分数与主观分数表现出高度的相关性和一致性。