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Multi-Focus Image Fusion via Distance-Weighted Regional Energy and Structure Tensor in NSCT Domain

In this paper, a multi-focus image fusion algorithm via the distance-weighted regional energy and structure tensor in non-subsampled contourlet transform domain is introduced. The distance-weighted regional energy-based fusion rule was used to deal with low-frequency components, and the structure te...

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Autores principales: Lv, Ming, Li, Liangliang, Jin, Qingxin, Jia, Zhenhong, Chen, Liangfu, Ma, Hongbing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346789/
https://www.ncbi.nlm.nih.gov/pubmed/37447984
http://dx.doi.org/10.3390/s23136135
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author Lv, Ming
Li, Liangliang
Jin, Qingxin
Jia, Zhenhong
Chen, Liangfu
Ma, Hongbing
author_facet Lv, Ming
Li, Liangliang
Jin, Qingxin
Jia, Zhenhong
Chen, Liangfu
Ma, Hongbing
author_sort Lv, Ming
collection PubMed
description In this paper, a multi-focus image fusion algorithm via the distance-weighted regional energy and structure tensor in non-subsampled contourlet transform domain is introduced. The distance-weighted regional energy-based fusion rule was used to deal with low-frequency components, and the structure tensor-based fusion rule was used to process high-frequency components; fused sub-bands were integrated with the inverse non-subsampled contourlet transform, and a fused multi-focus image was generated. We conducted a series of simulations and experiments on the multi-focus image public dataset Lytro; the experimental results of 20 sets of data show that our algorithm has significant advantages compared to advanced algorithms and that it can produce clearer and more informative multi-focus fusion images.
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spelling pubmed-103467892023-07-15 Multi-Focus Image Fusion via Distance-Weighted Regional Energy and Structure Tensor in NSCT Domain Lv, Ming Li, Liangliang Jin, Qingxin Jia, Zhenhong Chen, Liangfu Ma, Hongbing Sensors (Basel) Article In this paper, a multi-focus image fusion algorithm via the distance-weighted regional energy and structure tensor in non-subsampled contourlet transform domain is introduced. The distance-weighted regional energy-based fusion rule was used to deal with low-frequency components, and the structure tensor-based fusion rule was used to process high-frequency components; fused sub-bands were integrated with the inverse non-subsampled contourlet transform, and a fused multi-focus image was generated. We conducted a series of simulations and experiments on the multi-focus image public dataset Lytro; the experimental results of 20 sets of data show that our algorithm has significant advantages compared to advanced algorithms and that it can produce clearer and more informative multi-focus fusion images. MDPI 2023-07-04 /pmc/articles/PMC10346789/ /pubmed/37447984 http://dx.doi.org/10.3390/s23136135 Text en © 2023 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
Lv, Ming
Li, Liangliang
Jin, Qingxin
Jia, Zhenhong
Chen, Liangfu
Ma, Hongbing
Multi-Focus Image Fusion via Distance-Weighted Regional Energy and Structure Tensor in NSCT Domain
title Multi-Focus Image Fusion via Distance-Weighted Regional Energy and Structure Tensor in NSCT Domain
title_full Multi-Focus Image Fusion via Distance-Weighted Regional Energy and Structure Tensor in NSCT Domain
title_fullStr Multi-Focus Image Fusion via Distance-Weighted Regional Energy and Structure Tensor in NSCT Domain
title_full_unstemmed Multi-Focus Image Fusion via Distance-Weighted Regional Energy and Structure Tensor in NSCT Domain
title_short Multi-Focus Image Fusion via Distance-Weighted Regional Energy and Structure Tensor in NSCT Domain
title_sort multi-focus image fusion via distance-weighted regional energy and structure tensor in nsct domain
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346789/
https://www.ncbi.nlm.nih.gov/pubmed/37447984
http://dx.doi.org/10.3390/s23136135
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