BAYESIAN TOMOGRAPHIC RECONSTRUCTION FOR HIGH ANGLE ANNULAR DARK FIELD (HAADF) SCANNING TRANSMISSION ELECTRON MICROSCOP Y (STEM)

被引：0

作者：

Venkatakrishnan, Singanallur ^{[1
]}

Drummy, Lawrence ^{[2
]}

Jackson, Michael ^{[3
]}

De Graef, Marc ^{[4
]}

Simmons, Jeff ^{[2
]}

Bouman, Charles ^{[1
]}

机构：

[1] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA

[2] Wright Patterson Air Force Base, Air Force Res Lab, Dayton, OH USA

[3] BlueQuartz Software, Springboro, OH USA

[4] Carnegie Mellon Univ, Dept Mat Sci & Engn, Pittsburgh, PA 15213 USA

来源：

2012 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP) | 2012年

关键词：

Electron tomography; dark-field; Bayesian methods;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

HAADF-STEM data is increasingly being used in the physical sciences to study materials in 3D because it is free from the diffraction effects seen in Bright Field STEM data and satisfies the projection requirement for tomography. Typically, reconstruction is performed using Filtered Back Projection (FBP) or the SIRT algorithm. In this paper, we develop a Bayesian reconstruction algorithm for HAADF-STEM tomography which models the image formation, the noise characteristics of the measurement, and the inherent smoothness in the object. Reconstructions of polystyrene functionalized Titanium dioxide nano particle assemblies show results that are qualitatively superior to FBP and SIRT reconstructions, significantly suppressing artifacts and enhancing contrast.

引用

页码：680 / 683

页数：4

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