The Fusion of Multi-Focus Images Based on the Complex Shearlet Features-Motivated Generative Adversarial Network
The traditional methods for multi-focus image fusion, such as the typical multi-scale geometric analysis theory-based methods, are usually restricted by sparse representation ability and the transferring efficiency of the fusion rules for the captured features. Aiming to integrate the partially focu...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/5439935 |
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| _version_ | 1850235920278618112 |
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| author | Lei Wang ZhouQi Liu Jin Huang Cong Liu LongBo Zhang ChunXiang Liu |
| author_facet | Lei Wang ZhouQi Liu Jin Huang Cong Liu LongBo Zhang ChunXiang Liu |
| author_sort | Lei Wang |
| collection | DOAJ |
| description | The traditional methods for multi-focus image fusion, such as the typical multi-scale geometric analysis theory-based methods, are usually restricted by sparse representation ability and the transferring efficiency of the fusion rules for the captured features. Aiming to integrate the partially focused images into the fully focused image with high quality, the complex shearlet features-motivated generative adversarial network is constructed for multi-focus image fusion in this paper. Different from the popularly used wavelet, contourlet, and shearlet, the complex shearlet provides more flexible multiple scales, anisotropy, and directional sub-bands with the approximate shift invariance. Therefore, the features in complex shearlet domain are more effective. With of help of the generative adversarial network, the whole procedure of multi-focus fusion is modeled to be the process of adversarial learning. Finally, several experiments are implemented and the results prove that the proposed method outperforms the popularly used fusion algorithms in terms of four typical objective metrics and the comparison of visual appearance. |
| format | Article |
| id | doaj-art-193fd3db1e2d43d0b25401ff941421ea |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-193fd3db1e2d43d0b25401ff941421ea2025-08-20T02:02:06ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/54399355439935The Fusion of Multi-Focus Images Based on the Complex Shearlet Features-Motivated Generative Adversarial NetworkLei Wang0ZhouQi Liu1Jin Huang2Cong Liu3LongBo Zhang4ChunXiang Liu5School of Computer Science and Technology, Shandong University of Technology, Zibo 255000, ChinaSchool of Computer Science and Technology, Shandong University of Technology, Zibo 255000, ChinaSchool of Computer Science and Technology, Shandong University of Technology, Zibo 255000, ChinaSchool of Computer Science and Technology, Shandong University of Technology, Zibo 255000, ChinaSchool of Computer Science and Technology, Shandong University of Technology, Zibo 255000, ChinaAnhui Key Laboratory of Plant Resources and Plant Biology, Huaibei Normal University, Huaibei 235000, ChinaThe traditional methods for multi-focus image fusion, such as the typical multi-scale geometric analysis theory-based methods, are usually restricted by sparse representation ability and the transferring efficiency of the fusion rules for the captured features. Aiming to integrate the partially focused images into the fully focused image with high quality, the complex shearlet features-motivated generative adversarial network is constructed for multi-focus image fusion in this paper. Different from the popularly used wavelet, contourlet, and shearlet, the complex shearlet provides more flexible multiple scales, anisotropy, and directional sub-bands with the approximate shift invariance. Therefore, the features in complex shearlet domain are more effective. With of help of the generative adversarial network, the whole procedure of multi-focus fusion is modeled to be the process of adversarial learning. Finally, several experiments are implemented and the results prove that the proposed method outperforms the popularly used fusion algorithms in terms of four typical objective metrics and the comparison of visual appearance.http://dx.doi.org/10.1155/2021/5439935 |
| spellingShingle | Lei Wang ZhouQi Liu Jin Huang Cong Liu LongBo Zhang ChunXiang Liu The Fusion of Multi-Focus Images Based on the Complex Shearlet Features-Motivated Generative Adversarial Network Journal of Advanced Transportation |
| title | The Fusion of Multi-Focus Images Based on the Complex Shearlet Features-Motivated Generative Adversarial Network |
| title_full | The Fusion of Multi-Focus Images Based on the Complex Shearlet Features-Motivated Generative Adversarial Network |
| title_fullStr | The Fusion of Multi-Focus Images Based on the Complex Shearlet Features-Motivated Generative Adversarial Network |
| title_full_unstemmed | The Fusion of Multi-Focus Images Based on the Complex Shearlet Features-Motivated Generative Adversarial Network |
| title_short | The Fusion of Multi-Focus Images Based on the Complex Shearlet Features-Motivated Generative Adversarial Network |
| title_sort | fusion of multi focus images based on the complex shearlet features motivated generative adversarial network |
| url | http://dx.doi.org/10.1155/2021/5439935 |
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