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Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary

Multi-focus image fusion is an important method used to combine the focused parts from source multi-focus images into a single full-focus image. Currently, to address the problem of multi-focus image fusion, the key is on how to accurately detect the focus regions, especially when the source images...

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Autores principales: Wan, Hui, Tang, Xianlun, Zhu, Zhiqin, Li, Weisheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8534655/
https://www.ncbi.nlm.nih.gov/pubmed/34682086
http://dx.doi.org/10.3390/e23101362
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author Wan, Hui
Tang, Xianlun
Zhu, Zhiqin
Li, Weisheng
author_facet Wan, Hui
Tang, Xianlun
Zhu, Zhiqin
Li, Weisheng
author_sort Wan, Hui
collection PubMed
description Multi-focus image fusion is an important method used to combine the focused parts from source multi-focus images into a single full-focus image. Currently, to address the problem of multi-focus image fusion, the key is on how to accurately detect the focus regions, especially when the source images captured by cameras produce anisotropic blur and unregistration. This paper proposes a new multi-focus image fusion method based on the multi-scale decomposition of complementary information. Firstly, this method uses two groups of large-scale and small-scale decomposition schemes that are structurally complementary, to perform two-scale double-layer singular value decomposition of the image separately and obtain low-frequency and high-frequency components. Then, the low-frequency components are fused by a rule that integrates image local energy with edge energy. The high-frequency components are fused by the parameter-adaptive pulse-coupled neural network model (PA-PCNN), and according to the feature information contained in each decomposition layer of the high-frequency components, different detailed features are selected as the external stimulus input of the PA-PCNN. Finally, according to the two-scale decomposition of the source image that is structure complementary, and the fusion of high and low frequency components, two initial decision maps with complementary information are obtained. By refining the initial decision graph, the final fusion decision map is obtained to complete the image fusion. In addition, the proposed method is compared with 10 state-of-the-art approaches to verify its effectiveness. The experimental results show that the proposed method can more accurately distinguish the focused and non-focused areas in the case of image pre-registration and unregistration, and the subjective and objective evaluation indicators are slightly better than those of the existing methods.
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spelling pubmed-85346552021-10-23 Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary Wan, Hui Tang, Xianlun Zhu, Zhiqin Li, Weisheng Entropy (Basel) Article Multi-focus image fusion is an important method used to combine the focused parts from source multi-focus images into a single full-focus image. Currently, to address the problem of multi-focus image fusion, the key is on how to accurately detect the focus regions, especially when the source images captured by cameras produce anisotropic blur and unregistration. This paper proposes a new multi-focus image fusion method based on the multi-scale decomposition of complementary information. Firstly, this method uses two groups of large-scale and small-scale decomposition schemes that are structurally complementary, to perform two-scale double-layer singular value decomposition of the image separately and obtain low-frequency and high-frequency components. Then, the low-frequency components are fused by a rule that integrates image local energy with edge energy. The high-frequency components are fused by the parameter-adaptive pulse-coupled neural network model (PA-PCNN), and according to the feature information contained in each decomposition layer of the high-frequency components, different detailed features are selected as the external stimulus input of the PA-PCNN. Finally, according to the two-scale decomposition of the source image that is structure complementary, and the fusion of high and low frequency components, two initial decision maps with complementary information are obtained. By refining the initial decision graph, the final fusion decision map is obtained to complete the image fusion. In addition, the proposed method is compared with 10 state-of-the-art approaches to verify its effectiveness. The experimental results show that the proposed method can more accurately distinguish the focused and non-focused areas in the case of image pre-registration and unregistration, and the subjective and objective evaluation indicators are slightly better than those of the existing methods. MDPI 2021-10-19 /pmc/articles/PMC8534655/ /pubmed/34682086 http://dx.doi.org/10.3390/e23101362 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
Wan, Hui
Tang, Xianlun
Zhu, Zhiqin
Li, Weisheng
Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary
title Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary
title_full Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary
title_fullStr Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary
title_full_unstemmed Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary
title_short Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary
title_sort multi-focus image fusion method based on multi-scale decomposition of information complementary
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8534655/
https://www.ncbi.nlm.nih.gov/pubmed/34682086
http://dx.doi.org/10.3390/e23101362
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AT tangxianlun multifocusimagefusionmethodbasedonmultiscaledecompositionofinformationcomplementary
AT zhuzhiqin multifocusimagefusionmethodbasedonmultiscaledecompositionofinformationcomplementary
AT liweisheng multifocusimagefusionmethodbasedonmultiscaledecompositionofinformationcomplementary