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火山灰表面的定量分析:扩展景深(聚焦)算法在光学显微镜和扫描电子显微镜中的应用及三维重建

Quantitative analysis on volcanic ash surfaces: application of extended depth-of-field (focus) algorithm for light and scanning electron microscopy and 3D reconstruction.

作者信息

Ersoy Orkun, Aydar Erkan, Gourgaud Alain, Bayhan Hasan

机构信息

Department of Geological Engineering, Hacettepe University, 06532 Beytepe-Ankara, Turkey.

出版信息

Micron. 2008;39(2):128-36. doi: 10.1016/j.micron.2006.11.010. Epub 2006 Dec 27.

DOI:10.1016/j.micron.2006.11.010
PMID:17208002
Abstract

The depth-of-field mainly affects the image quality either in scanning electron microscopy (SEM) or conventional light microscopy. The limited depth-of-field handicap of microscopy imaging can be used for obtaining "optically sectioned" specimens by moving the object along the optical axis. In this study, multiple images corresponding to different object planes were taken in order to overcome limited depth-of-field on conventional light microscope and SEM, estimation of an elevation surface and 3D reconstruction of different type volcanic ash surfaces. We used extended depth-of-field, a fusion algorithm that combines those images into one single sharp composite. Because of larger depth-of-field, we got higher-quality results even with image stacks taken by SEM with a fixed aperture in variable pressure mode. We calculated roughness descriptors, quadtree decomposition and greylevel standard deviation (sGL) and analyzed the shape of polar plots based on gradient analysis of constructed depth-maps. Furthermore, we calculated fractal dimensions of surfaces. Correlation analysis was performed to measure how these quantitative variables are related with different type ash surfaces. Roughness descriptors, quadtree decomposition, sGL and fractal dimension discriminate different types of volcanic ash surfaces.

摘要

景深主要影响扫描电子显微镜(SEM)或传统光学显微镜下的图像质量。显微镜成像景深有限的缺陷可通过沿光轴移动物体来获取“光学切片”标本。在本研究中,为克服传统光学显微镜和SEM景深有限的问题,拍摄了对应不同物平面的多张图像,以估计火山灰表面的高程并进行不同类型火山灰表面的三维重建。我们使用了扩展景深,这是一种将这些图像融合成一张清晰合成图像的算法。由于景深较大,即使在可变压力模式下使用固定孔径的SEM拍摄图像堆栈,我们也获得了更高质量的结果。我们计算了粗糙度描述符、四叉树分解和灰度标准差(sGL),并基于构建的深度图的梯度分析对极坐标图的形状进行了分析。此外,我们还计算了表面的分形维数。进行了相关性分析,以测量这些定量变量与不同类型火山灰表面的关系。粗糙度描述符、四叉树分解、sGL和分形维数能够区分不同类型的火山灰表面。

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