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Hybrid Sparsity Model for Fast Terahertz Imaging

In order to shorten the long-term image acquisition time of the terahertz time domain spectroscopy imaging system while ensuring the imaging quality, a hybrid sparsity model (HSM) is proposed for fast terahertz imaging in this paper, which incorporates both intrinsic sparsity prior and nonlocal self...

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Detalles Bibliográficos
Autores principales: Ren, Xiaozhen, Bai, Yanwen, Jiang, Yuying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8538983/
https://www.ncbi.nlm.nih.gov/pubmed/34683232
http://dx.doi.org/10.3390/mi12101181
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author Ren, Xiaozhen
Bai, Yanwen
Jiang, Yuying
author_facet Ren, Xiaozhen
Bai, Yanwen
Jiang, Yuying
author_sort Ren, Xiaozhen
collection PubMed
description In order to shorten the long-term image acquisition time of the terahertz time domain spectroscopy imaging system while ensuring the imaging quality, a hybrid sparsity model (HSM) is proposed for fast terahertz imaging in this paper, which incorporates both intrinsic sparsity prior and nonlocal self-similarity constraints in a unified statistical model. In HSM, a weighted exponentiation shift-invariant wavelet transform is introduced to enhance the sparsity of the terahertz image. Simultaneously, the nonlocal self-similarity by means of the three-dimensional sparsity in the transform domain is exploited to ensure high-quality terahertz image reconstruction. Finally, a new split Bregman-based iteration algorithm is developed to solve the terahertz imaging model more efficiently. Experiments are presented to verify the effectiveness of the proposed approach.
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spelling pubmed-85389832021-10-24 Hybrid Sparsity Model for Fast Terahertz Imaging Ren, Xiaozhen Bai, Yanwen Jiang, Yuying Micromachines (Basel) Article In order to shorten the long-term image acquisition time of the terahertz time domain spectroscopy imaging system while ensuring the imaging quality, a hybrid sparsity model (HSM) is proposed for fast terahertz imaging in this paper, which incorporates both intrinsic sparsity prior and nonlocal self-similarity constraints in a unified statistical model. In HSM, a weighted exponentiation shift-invariant wavelet transform is introduced to enhance the sparsity of the terahertz image. Simultaneously, the nonlocal self-similarity by means of the three-dimensional sparsity in the transform domain is exploited to ensure high-quality terahertz image reconstruction. Finally, a new split Bregman-based iteration algorithm is developed to solve the terahertz imaging model more efficiently. Experiments are presented to verify the effectiveness of the proposed approach. MDPI 2021-09-29 /pmc/articles/PMC8538983/ /pubmed/34683232 http://dx.doi.org/10.3390/mi12101181 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ren, Xiaozhen
Bai, Yanwen
Jiang, Yuying
Hybrid Sparsity Model for Fast Terahertz Imaging
title Hybrid Sparsity Model for Fast Terahertz Imaging
title_full Hybrid Sparsity Model for Fast Terahertz Imaging
title_fullStr Hybrid Sparsity Model for Fast Terahertz Imaging
title_full_unstemmed Hybrid Sparsity Model for Fast Terahertz Imaging
title_short Hybrid Sparsity Model for Fast Terahertz Imaging
title_sort hybrid sparsity model for fast terahertz imaging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8538983/
https://www.ncbi.nlm.nih.gov/pubmed/34683232
http://dx.doi.org/10.3390/mi12101181
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AT baiyanwen hybridsparsitymodelforfastterahertzimaging
AT jiangyuying hybridsparsitymodelforfastterahertzimaging