Compressed sensing electron tomography using adaptive dictionaries: a simulation study

AlAfeef, A., Cockshott, P., MacLaren, I. and McVitie, S. (2014) Compressed sensing electron tomography using adaptive dictionaries: a simulation study. Journal of Physics: Conference Series, 522(1), 012021. (doi: 10.1088/1742-6596/522/1/012021)

Abstract

Electron tomography (ET) is an increasingly important technique for examining the three-dimensional morphologies of nanostructures. ET involves the acquisition of a set of 2D projection images to be reconstructed into a volumetric image by solving an inverse problem. However, due to limitations in the acquisition process this inverse problem is considered ill-posed (i.e., no unique solution exists). Furthermore reconstruction usually suffers from missing wedge artifacts (e.g., star, fan, blurring, and elongation artifacts). Compressed sensing (CS) has recently been applied to ET and showed promising results for reducing missing wedge artifacts caused by limited angle sampling. CS uses a nonlinear reconstruction algorithm that employs image sparsity as a priori knowledge to improve the accuracy of density reconstruction from a relatively small number of projections compared to other reconstruction techniques. However, The performance of CS recovery depends heavily on the degree of sparsity of the reconstructed image in the selected transform domain. Prespecified transformations such as spatial gradients provide sparse image representation, while synthesising the sparsifying transform based on the properties of the particular specimen may give even sparser results and can extend the application of CS to specimens that can not be sparsely represented with other transforms such as Total variation (TV). In this work, we show that CS reconstruction in ET can be significantly improved by tailoring the sparsity representation using a sparse dictionary learning principle.

• Item Type: Articles
• Additional Information: Electron Microscopy and Analysis Group Conference 2013 (EMAG2013) 3–6 Sep 2013, York, United Kingdom
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: McVitie, Professor Stephen and Al-Afeef, Mr Ala and Cockshott, Dr William and MacLaren, Dr Ian
• Authors: AlAfeef, A., Cockshott, P., MacLaren, I., and McVitie, S.
• College/School: College of Science and Engineering > School of Computing Science; College of Science and Engineering > School of Physics and Astronomy
• Journal Name: Journal of Physics: Conference Series
• Publisher: IOP Publishing
• ISSN: 1742-6588
• ISSN (Online): 1742-6596
• Copyright Holders: Copyright © 2014 IOP Publishing Ltd
• First Published: First published in Journal of Physics: Conference Series 522(1):012021
• Publisher Policy: Reproduced under a Creative Commons License