3M: A Multi-Scale and Multi-Directional Method for Multi-Focus Image Fusion

Based on the analysis of the multi-scale-based and sparse representation-based multi-focus image fusion method, we believe that a multi-scale fusion method should consider the directivity of high/low frequency sub-bands and low computational complexity, ensuring the effectiveness and efficiency. The...

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Main Authors: Bingzhe Wei, Xiangchu Feng, Weiwei Wang
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9386078/
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spelling doaj-c59af6d0ee6240e68dfec30c7ce99d872021-04-05T17:39:25ZengIEEEIEEE Access2169-35362021-01-019485314854310.1109/ACCESS.2021.306877093860783M: A Multi-Scale and Multi-Directional Method for Multi-Focus Image FusionBingzhe Wei0https://orcid.org/0000-0001-6327-9211Xiangchu Feng1https://orcid.org/0000-0002-3463-2060Weiwei Wang2https://orcid.org/0000-0002-6985-2784School of Mathematics and Statistics, Xidian University, Xi’an, ChinaSchool of Mathematics and Statistics, Xidian University, Xi’an, ChinaSchool of Mathematics and Statistics, Xidian University, Xi’an, ChinaBased on the analysis of the multi-scale-based and sparse representation-based multi-focus image fusion method, we believe that a multi-scale fusion method should consider the directivity of high/low frequency sub-bands and low computational complexity, ensuring the effectiveness and efficiency. Therefore, we propose a novel multi-focus image fusion algorithm based on multi-scale and multi-directional dictionaries. In the proposed method, source images are decomposed by multi-scale transform(MST) to obtain high/low frequency sub-bands. For the high-frequency part, a direction contrast-based fusion rule is presented. For the low-frequency part, the image patches are divided into strong/weak information patches. The strong information patches are merged with a fusion approach based on directional dictionaries, while the weak information patches are fused using weighted average. Finally, the fused image is obtained by performing the inverse MST. The experimental results show that the proposed approach extracts more important effective information from source images, and the computational efficiency is greatly improved.https://ieeexplore.ieee.org/document/9386078/Multi-focus image fusionmulti-scale transformdirectionalityfusion rule
collection DOAJ
language English
format Article
sources DOAJ
author Bingzhe Wei
Xiangchu Feng
Weiwei Wang
spellingShingle Bingzhe Wei
Xiangchu Feng
Weiwei Wang
3M: A Multi-Scale and Multi-Directional Method for Multi-Focus Image Fusion
IEEE Access
Multi-focus image fusion
multi-scale transform
directionality
fusion rule
author_facet Bingzhe Wei
Xiangchu Feng
Weiwei Wang
author_sort Bingzhe Wei
title 3M: A Multi-Scale and Multi-Directional Method for Multi-Focus Image Fusion
title_short 3M: A Multi-Scale and Multi-Directional Method for Multi-Focus Image Fusion
title_full 3M: A Multi-Scale and Multi-Directional Method for Multi-Focus Image Fusion
title_fullStr 3M: A Multi-Scale and Multi-Directional Method for Multi-Focus Image Fusion
title_full_unstemmed 3M: A Multi-Scale and Multi-Directional Method for Multi-Focus Image Fusion
title_sort 3m: a multi-scale and multi-directional method for multi-focus image fusion
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2021-01-01
description Based on the analysis of the multi-scale-based and sparse representation-based multi-focus image fusion method, we believe that a multi-scale fusion method should consider the directivity of high/low frequency sub-bands and low computational complexity, ensuring the effectiveness and efficiency. Therefore, we propose a novel multi-focus image fusion algorithm based on multi-scale and multi-directional dictionaries. In the proposed method, source images are decomposed by multi-scale transform(MST) to obtain high/low frequency sub-bands. For the high-frequency part, a direction contrast-based fusion rule is presented. For the low-frequency part, the image patches are divided into strong/weak information patches. The strong information patches are merged with a fusion approach based on directional dictionaries, while the weak information patches are fused using weighted average. Finally, the fused image is obtained by performing the inverse MST. The experimental results show that the proposed approach extracts more important effective information from source images, and the computational efficiency is greatly improved.
topic Multi-focus image fusion
multi-scale transform
directionality
fusion rule
url https://ieeexplore.ieee.org/document/9386078/
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AT xiangchufeng 3mamultiscaleandmultidirectionalmethodformultifocusimagefusion
AT weiweiwang 3mamultiscaleandmultidirectionalmethodformultifocusimagefusion
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