Optimization of EPMA Image Reconstruction Based on Generalized Orthogonal Matching Pursuit Algorithm
Authors: 
Anan Jin, Xiang LiAuthors Info & Claims
Pages 144 - 150
Abstract
In the realm of Compressed Sensing (CS), Orthogonal Matching Pursuit (OMP) algorithm is widely acknowledged. The generalized Orthogonal Matching Pursuit (gOMP) algorithm is an improved OMP approach. However, it demonstrates low reconstruction accuracy for electron probe images based on our experiments. This paper enhances gOMP by adjusting the algorithm parameters according to the characteristics of electron probe images. An optimal sparsity (K) and the atom selection numbers (S) matching with best parameters for gOMP is selected through experiments. Besides that, Fourier measurement matrix has been identified after evaluation of various measurement matrices. The improved algorithm, Fourier's generalized Orthogonal Matching Pursuit (FgOMP), is constructed based on these findings. The simulation results show that the proposed algorithm can achieve Super Resolution Recovery requirements and provide a significant higher reconstruction quality for electronic probe images than the original gOMP algorithm and other relevant algorithms.
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Index Terms
- Optimization of EPMA Image Reconstruction Based on Generalized Orthogonal Matching Pursuit Algorithm
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Published In
April 2020
234 pages
ISBN:9781450377713
DOI:10.1145/3398329
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In-Cooperation
- University of Salamanca: University of Salamanca
- The University of Adelaide, Australia
- Edinburgh Napier University, UK: Edinburgh Napier University, UK
- University of Sydney Australia
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Association for Computing Machinery
New York, NY, United States
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Published: 31 May 2020
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- Research
- Refereed limited
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- National Natural Science Foundation of China
- Jiangxi Nuclear Geoscience Data Science and System Engineering Technology Research Center Open Fund
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CNIOT2020
CNIOT2020: 2020 International Conference on Computing, Networks and Internet of Things
April 24 - 26, 2020
Sanya, China
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CNIOT '20 Paper Acceptance Rate 39 of 82 submissions, 48%;
Overall Acceptance Rate 39 of 82 submissions, 48%
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