Effective Multifocus Image Fusion Based on HVS and BP Neural Network
The aim of multifocus image fusion is to fuse the images taken from the same scene with different focuses to obtain a resultant image with all objects in focus. In this paper, a novel multifocus image fusion method based on human visual system (HVS) and back propagation (BP) neural network is presen...
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| Format: | Online |
| Language: | English |
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Hindawi Publishing Corporation
2014
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3933522/ |
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pubmed-3933522 |
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pubmed-39335222014-03-30 Effective Multifocus Image Fusion Based on HVS and BP Neural Network Yang, Yong Zheng, Wenjuan Huang, Shuying Research Article The aim of multifocus image fusion is to fuse the images taken from the same scene with different focuses to obtain a resultant image with all objects in focus. In this paper, a novel multifocus image fusion method based on human visual system (HVS) and back propagation (BP) neural network is presented. Three features which reflect the clarity of a pixel are firstly extracted and used to train a BP neural network to determine which pixel is clearer. The clearer pixels are then used to construct the initial fused image. Thirdly, the focused regions are detected by measuring the similarity between the source images and the initial fused image followed by morphological opening and closing operations. Finally, the final fused image is obtained by a fusion rule for those focused regions. Experimental results show that the proposed method can provide better performance and outperform several existing popular fusion methods in terms of both objective and subjective evaluations. Hindawi Publishing Corporation 2014-02-06 /pmc/articles/PMC3933522/ /pubmed/24683327 http://dx.doi.org/10.1155/2014/281073 Text en Copyright © 2014 Yong Yang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Yang, Yong Zheng, Wenjuan Huang, Shuying |
| spellingShingle |
Yang, Yong Zheng, Wenjuan Huang, Shuying Effective Multifocus Image Fusion Based on HVS and BP Neural Network |
| author_facet |
Yang, Yong Zheng, Wenjuan Huang, Shuying |
| author_sort |
Yang, Yong |
| title |
Effective Multifocus Image Fusion Based on HVS and BP Neural Network |
| title_short |
Effective Multifocus Image Fusion Based on HVS and BP Neural Network |
| title_full |
Effective Multifocus Image Fusion Based on HVS and BP Neural Network |
| title_fullStr |
Effective Multifocus Image Fusion Based on HVS and BP Neural Network |
| title_full_unstemmed |
Effective Multifocus Image Fusion Based on HVS and BP Neural Network |
| title_sort |
effective multifocus image fusion based on hvs and bp neural network |
| description |
The aim of multifocus image fusion is to fuse the images taken from the same scene with different focuses to obtain a resultant image with all objects in focus. In this paper, a novel multifocus image fusion method based on human visual system (HVS) and back propagation (BP) neural network is presented. Three features which reflect the clarity of a pixel are firstly extracted and used to train a BP neural network to determine which pixel is clearer. The clearer pixels are then used to construct the initial fused image. Thirdly, the focused regions are detected by measuring the similarity between the source images and the initial fused image followed by morphological opening and closing operations. Finally, the final fused image is obtained by a fusion rule for those focused regions. Experimental results show that the proposed method can provide better performance and outperform several existing popular fusion methods in terms of both objective and subjective evaluations. |
| publisher |
Hindawi Publishing Corporation |
| publishDate |
2014 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3933522/ |
| _version_ |
1612061470367940608 |