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通过傅里叶分析进行空间图像分辨率评估(SIRAF)。

Spatial Image Resolution Assessment by Fourier Analysis (SIRAF).

作者信息

Brostrøm Anders, Mølhave Kristian

机构信息

Technical University of Denmark, DTU Nanolab - National Centre for Nano Fabrication and Characterization, Fysikvej, Building 307, 2800Kgs. Lyngby, Denmark.

出版信息

Microsc Microanal. 2022 Mar 3:1-9. doi: 10.1017/S1431927622000228.

DOI:10.1017/S1431927622000228
PMID:35236536
Abstract

Determining spatial resolution from images is crucial when optimizing focus, determining smallest resolvable object, and assessing size measurement uncertainties. However, no standard algorithm exists to measure resolution from electron microscopy (EM) images, though several have been proposed, where most require user decisions. We present the Spatial Image Resolution Assessment by Fourier analysis (SIRAF) algorithm that uses fast Fourier transform analysis to estimate resolution directly from a single image without user inputs. The method is derived from the underlying assumption that objects display intensity transitions, resembling a step function blurred by a Gaussian point spread function. This hypothesis is tested and verified on simulated EM images with known resolution. To identify potential pitfalls, the algorithm is also tested on simulated images with a variety of settings, and on real SEM images acquired at different magnification and defocus settings. Finally, the versatility of the method is investigated by assessing resolution in images from several microscopy techniques. It is concluded that the algorithm can assess resolution from a large selection of image types, thereby providing a measure of this fundamental image parameter. It may also improve autofocus methods and guide the optimization of magnification settings when balancing spatial resolution and field of view.

摘要

在优化聚焦、确定最小可分辨物体以及评估尺寸测量不确定性时,从图像中确定空间分辨率至关重要。然而,尽管已经提出了几种方法,但目前尚无用于测量电子显微镜(EM)图像分辨率的标准算法,其中大多数方法都需要用户做出决策。我们提出了一种通过傅里叶分析进行空间图像分辨率评估(SIRAF)的算法,该算法使用快速傅里叶变换分析直接从单幅图像中估计分辨率,无需用户输入。该方法基于这样一个基本假设:物体显示出强度跃迁,类似于由高斯点扩散函数模糊的阶跃函数。这一假设在具有已知分辨率的模拟EM图像上进行了测试和验证。为了识别潜在的陷阱,该算法还在具有各种设置的模拟图像以及在不同放大倍数和散焦设置下采集的真实扫描电子显微镜(SEM)图像上进行了测试。最后,通过评估来自几种显微镜技术的图像分辨率来研究该方法的通用性。得出的结论是,该算法可以评估多种图像类型的分辨率,从而提供这一基本图像参数的度量。它还可能改进自动聚焦方法,并在平衡空间分辨率和视野时指导放大倍数设置的优化。

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