Multifocus Image Fusion Based on Extreme Learning Machine and Human Visual System
Multifocus image fusion generates a single image by combining redundant and complementary information of multiple images coming from the same scene. The combination includes more information of the scene than any of the individual source images. In this paper, a novel multifocus image fusion method...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2017-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7906593/ |
| _version_ | 1831798977495826432 |
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| author | Yong Yang Mei Yang Shuying Huang Yue Que Min Ding Jun Sun |
| author_facet | Yong Yang Mei Yang Shuying Huang Yue Que Min Ding Jun Sun |
| author_sort | Yong Yang |
| collection | DOAJ |
| description | Multifocus image fusion generates a single image by combining redundant and complementary information of multiple images coming from the same scene. The combination includes more information of the scene than any of the individual source images. In this paper, a novel multifocus image fusion method based on extreme learning machine (ELM) and human visual system is proposed. Three visual features that reflect the clarity of a pixel are first extracted and used to train the ELM to judge which pixel is clearer. The clearer pixels are then used to construct the initial fused image. Second, we measure the similarity between the source image and the initial fused image and perform morphological opening and closing operations to obtain the focused regions. Lastly, the final fused image is achieved by employing a fusion rule in the focus regions and the initial fused image. Experimental results indicate that the proposed method is more effective and better than other series of existing popular fusion methods in terms of both subjective and objective evaluations. |
| first_indexed | 2024-12-22T17:36:48Z |
| format | Article |
| id | doaj.art-8009ee4234bc4a50bd2007ff8ed55787 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T17:36:48Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8009ee4234bc4a50bd2007ff8ed557872022-12-21T18:18:30ZengIEEEIEEE Access2169-35362017-01-0156989700010.1109/ACCESS.2017.26961197906593Multifocus Image Fusion Based on Extreme Learning Machine and Human Visual SystemYong Yang0https://orcid.org/0000-0001-9467-0942Mei Yang1Shuying Huang2https://orcid.org/0000-0003-2771-8461Yue Que3Min Ding4Jun Sun5School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, ChinaSchool of Information Technology, Jiangxi University of Finance and Economics, Nanchang, ChinaSchool of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang, ChinaSchool of Information Technology, Jiangxi University of Finance and Economics, Nanchang, ChinaSchool of Information Technology, Jiangxi University of Finance and Economics, Nanchang, ChinaSchool of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang, ChinaMultifocus image fusion generates a single image by combining redundant and complementary information of multiple images coming from the same scene. The combination includes more information of the scene than any of the individual source images. In this paper, a novel multifocus image fusion method based on extreme learning machine (ELM) and human visual system is proposed. Three visual features that reflect the clarity of a pixel are first extracted and used to train the ELM to judge which pixel is clearer. The clearer pixels are then used to construct the initial fused image. Second, we measure the similarity between the source image and the initial fused image and perform morphological opening and closing operations to obtain the focused regions. Lastly, the final fused image is achieved by employing a fusion rule in the focus regions and the initial fused image. Experimental results indicate that the proposed method is more effective and better than other series of existing popular fusion methods in terms of both subjective and objective evaluations.https://ieeexplore.ieee.org/document/7906593/Multifocus image fusionhuman visual systemextreme learning machinefocused regions |
| spellingShingle | Yong Yang Mei Yang Shuying Huang Yue Que Min Ding Jun Sun Multifocus Image Fusion Based on Extreme Learning Machine and Human Visual System IEEE Access Multifocus image fusion human visual system extreme learning machine focused regions |
| title | Multifocus Image Fusion Based on Extreme Learning Machine and Human Visual System |
| title_full | Multifocus Image Fusion Based on Extreme Learning Machine and Human Visual System |
| title_fullStr | Multifocus Image Fusion Based on Extreme Learning Machine and Human Visual System |
| title_full_unstemmed | Multifocus Image Fusion Based on Extreme Learning Machine and Human Visual System |
| title_short | Multifocus Image Fusion Based on Extreme Learning Machine and Human Visual System |
| title_sort | multifocus image fusion based on extreme learning machine and human visual system |
| topic | Multifocus image fusion human visual system extreme learning machine focused regions |
| url | https://ieeexplore.ieee.org/document/7906593/ |
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