A multi‐focus image fusion method based on multi‐source joint layering and convolutional sparse representation
Abstract In this paper, a new Multi‐Focus Image Fusion (MFIF) method based on multi‐source joint layering and Convolutional Sparse Representation (CSR) is proposed. Based on the characteristics of multi‐focus source images, a multi‐source joint layering regularization model was designed to divide th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.12345 |
| _version_ | 1828147417234538496 |
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| author | Yanxiang Hu Zhijie Chen Bo Zhang Lifeng Ma Jiaqi Li |
| author_facet | Yanxiang Hu Zhijie Chen Bo Zhang Lifeng Ma Jiaqi Li |
| author_sort | Yanxiang Hu |
| collection | DOAJ |
| description | Abstract In this paper, a new Multi‐Focus Image Fusion (MFIF) method based on multi‐source joint layering and Convolutional Sparse Representation (CSR) is proposed. Based on the characteristics of multi‐focus source images, a multi‐source joint layering regularization model was designed to divide the sources into a common base‐layer and respective focus detail‐layers. This strategy can overcome the defects caused by source layering separately effectively. In detail‐layer fusion, CSR was employed to extract and global features. It can avoid detail blur and high computational cost caused by image blocking in the conventional sparse representation model. The proposed detail‐layer fusion rule combined the CSR coefficient maps pairwise with the window based select‐max rule. In the experiments, the optimal layering parameter was selected by experiments at first, and then five recently proposed specific MFIF or general image fusion algorithms were contrasted with the proposed method by plenty of subjective and objective experimental comparisons. The experimental results demonstrated the superiority of the authors’ method. |
| first_indexed | 2024-04-11T21:00:37Z |
| format | Article |
| id | doaj.art-ed5facea01fd4c998ec33152acede0dc |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-11T21:00:37Z |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-ed5facea01fd4c998ec33152acede0dc2022-12-22T04:03:32ZengWileyIET Image Processing1751-96591751-96672022-01-0116121622810.1049/ipr2.12345A multi‐focus image fusion method based on multi‐source joint layering and convolutional sparse representationYanxiang Hu0Zhijie Chen1Bo Zhang2Lifeng Ma3Jiaqi Li4College of Computer and Information Engineering Tianjin Normal University Tianjin ChinaCollege of Computer and Information Engineering Tianjin Normal University Tianjin ChinaCollege of Computer and Information Engineering Tianjin Normal University Tianjin ChinaCollege of Computer and Information Engineering Tianjin Normal University Tianjin ChinaCollege of Computer and Information Engineering Tianjin Normal University Tianjin ChinaAbstract In this paper, a new Multi‐Focus Image Fusion (MFIF) method based on multi‐source joint layering and Convolutional Sparse Representation (CSR) is proposed. Based on the characteristics of multi‐focus source images, a multi‐source joint layering regularization model was designed to divide the sources into a common base‐layer and respective focus detail‐layers. This strategy can overcome the defects caused by source layering separately effectively. In detail‐layer fusion, CSR was employed to extract and global features. It can avoid detail blur and high computational cost caused by image blocking in the conventional sparse representation model. The proposed detail‐layer fusion rule combined the CSR coefficient maps pairwise with the window based select‐max rule. In the experiments, the optimal layering parameter was selected by experiments at first, and then five recently proposed specific MFIF or general image fusion algorithms were contrasted with the proposed method by plenty of subjective and objective experimental comparisons. The experimental results demonstrated the superiority of the authors’ method.https://doi.org/10.1049/ipr2.12345Optical, image and video signal processingSensor fusionComputer vision and image processing techniques |
| spellingShingle | Yanxiang Hu Zhijie Chen Bo Zhang Lifeng Ma Jiaqi Li A multi‐focus image fusion method based on multi‐source joint layering and convolutional sparse representation IET Image Processing Optical, image and video signal processing Sensor fusion Computer vision and image processing techniques |
| title | A multi‐focus image fusion method based on multi‐source joint layering and convolutional sparse representation |
| title_full | A multi‐focus image fusion method based on multi‐source joint layering and convolutional sparse representation |
| title_fullStr | A multi‐focus image fusion method based on multi‐source joint layering and convolutional sparse representation |
| title_full_unstemmed | A multi‐focus image fusion method based on multi‐source joint layering and convolutional sparse representation |
| title_short | A multi‐focus image fusion method based on multi‐source joint layering and convolutional sparse representation |
| title_sort | multi focus image fusion method based on multi source joint layering and convolutional sparse representation |
| topic | Optical, image and video signal processing Sensor fusion Computer vision and image processing techniques |
| url | https://doi.org/10.1049/ipr2.12345 |
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