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Two-Level Structural Sparsity Regularization for Identifying Lattices and Defects in Noisy Images by Li, Xin; Belianinov, Alex; Dyck, Ondrej; Jesse, Stephen; Park, Chiwoo is a scholarly article available to read on EtoBox.

What is Two-Level Structural Sparsity Regularization for Identifying Lattices and Defects in Noisy Images about?

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

Author

Li, Xin; Belianinov, Alex; Dyck, Ondrej; Jesse, Stephen; Park, Chiwoo

Published

2016

Language

EN