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GWRA: grey wolf based reconstruction algorithm for compressive sensing signals
The recent advances in compressive sensing (CS) based solutions make it a promising technique for signal acquisition, image processing and other types of data compression needs. In CS, the most challenging problem is to design an accurate and efficient algorithm for reconstructing the original data....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924449/ https://www.ncbi.nlm.nih.gov/pubmed/33816870 http://dx.doi.org/10.7717/peerj-cs.217 |
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author | Aziz, Ahmed Singh, Karan Elsawy, Ahmed Osamy, Walid Khedr, Ahmed M. |
author_facet | Aziz, Ahmed Singh, Karan Elsawy, Ahmed Osamy, Walid Khedr, Ahmed M. |
author_sort | Aziz, Ahmed |
collection | PubMed |
description | The recent advances in compressive sensing (CS) based solutions make it a promising technique for signal acquisition, image processing and other types of data compression needs. In CS, the most challenging problem is to design an accurate and efficient algorithm for reconstructing the original data. Greedy-based reconstruction algorithms proved themselves as a good solution to this problem because of their fast implementation and low complex computations. In this paper, we propose a new optimization algorithm called grey wolf reconstruction algorithm (GWRA). GWRA is inspired from the benefits of integrating both the reversible greedy algorithm and the grey wolf optimizer algorithm. The effectiveness of GWRA technique is demonstrated and validated through rigorous simulations. The simulation results show that GWRA significantly exceeds the greedy-based reconstruction algorithms such as sum product, orthogonal matching pursuit, compressive sampling matching pursuit and filtered back projection and swarm based techniques such as BA and PSO in terms of reducing the reconstruction error, the mean absolute percentage error and the average normalized mean squared error. |
format | Online Article Text |
id | pubmed-7924449 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79244492021-04-02 GWRA: grey wolf based reconstruction algorithm for compressive sensing signals Aziz, Ahmed Singh, Karan Elsawy, Ahmed Osamy, Walid Khedr, Ahmed M. PeerJ Comput Sci Artificial Intelligence The recent advances in compressive sensing (CS) based solutions make it a promising technique for signal acquisition, image processing and other types of data compression needs. In CS, the most challenging problem is to design an accurate and efficient algorithm for reconstructing the original data. Greedy-based reconstruction algorithms proved themselves as a good solution to this problem because of their fast implementation and low complex computations. In this paper, we propose a new optimization algorithm called grey wolf reconstruction algorithm (GWRA). GWRA is inspired from the benefits of integrating both the reversible greedy algorithm and the grey wolf optimizer algorithm. The effectiveness of GWRA technique is demonstrated and validated through rigorous simulations. The simulation results show that GWRA significantly exceeds the greedy-based reconstruction algorithms such as sum product, orthogonal matching pursuit, compressive sampling matching pursuit and filtered back projection and swarm based techniques such as BA and PSO in terms of reducing the reconstruction error, the mean absolute percentage error and the average normalized mean squared error. PeerJ Inc. 2019-09-02 /pmc/articles/PMC7924449/ /pubmed/33816870 http://dx.doi.org/10.7717/peerj-cs.217 Text en © 2019 Aziz et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Aziz, Ahmed Singh, Karan Elsawy, Ahmed Osamy, Walid Khedr, Ahmed M. GWRA: grey wolf based reconstruction algorithm for compressive sensing signals |
title | GWRA: grey wolf based reconstruction algorithm for compressive sensing signals |
title_full | GWRA: grey wolf based reconstruction algorithm for compressive sensing signals |
title_fullStr | GWRA: grey wolf based reconstruction algorithm for compressive sensing signals |
title_full_unstemmed | GWRA: grey wolf based reconstruction algorithm for compressive sensing signals |
title_short | GWRA: grey wolf based reconstruction algorithm for compressive sensing signals |
title_sort | gwra: grey wolf based reconstruction algorithm for compressive sensing signals |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924449/ https://www.ncbi.nlm.nih.gov/pubmed/33816870 http://dx.doi.org/10.7717/peerj-cs.217 |
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