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March 2018 Two-level structural sparsity regularization for identifying lattices and defects in noisy images
Xin Li, Alex Belianinov, Ondrej Dyck, Stephen Jesse, Chiwoo Park
Ann. Appl. Stat. 12(1): 348-377 (March 2018). DOI: 10.1214/17-AOAS1096

Abstract

This paper presents a regularized regression model with a two-level structural sparsity penalty applied to locate individual atoms in a noisy scanning transmission electron microscopy image (STEM). In crystals, the locations of atoms is symmetric, condensed into a few lattice groups. Therefore, by identifying the underlying lattice in a given image, individual atoms can be accurately located. We propose to formulate the identification of the lattice groups as a sparse group selection problem. Furthermore, real atomic scale images contain defects and vacancies, so atomic identification based solely on a lattice group may result in false positives and false negatives. To minimize error, model includes an individual sparsity regularization in addition to the group sparsity for a within-group selection, which results in a regression model with a two-level sparsity regularization. We propose a modification of the group orthogonal matching pursuit (gOMP) algorithm with a thresholding step to solve the atom finding problem. The convergence and statistical analyses of the proposed algorithm are presented. The proposed algorithm is also evaluated through numerical experiments with simulated images. The applicability of the algorithm on determination of atom structures and identification of imaging distortions and atomic defects was demonstrated using three real STEM images. We believe this is an important step toward automatic phase identification and assignment with the advent of genomic databases for materials.

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Xin Li. Alex Belianinov. Ondrej Dyck. Stephen Jesse. Chiwoo Park. "Two-level structural sparsity regularization for identifying lattices and defects in noisy images." Ann. Appl. Stat. 12 (1) 348 - 377, March 2018. https://doi.org/10.1214/17-AOAS1096

Information

Received: 1 April 2017; Revised: 1 August 2017; Published: March 2018
First available in Project Euclid: 9 March 2018

zbMATH: 06894710
MathSciNet: MR3773397
Digital Object Identifier: 10.1214/17-AOAS1096

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.12 • No. 1 • March 2018
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