The DOI matches its Crossref record; 39 of the 41 references it cites were found in a registry or an index.
Multilevel Features Convolutional Neural Network for Multifocus Image Fusion
Abstract
Multifocus image fusion is an important technique that aims to generate a single clean image by fusing multiple input images. In this paper, we propose a novel multilevel features convolutional neural network (MLFCNN) architecture for image fusion. In the MLFCNN model, all features learned from previous layers are passed to the subsequent layer. Inside every path between the previous layer and the subsequent layer, we add a 1 × 1 convolution module to reduce the redundancy. In our method, the source images first are fed to our pre-trained MLFCNN model to obtain the initial focus map. Then, the initial focus map is performed by morphological opening and closing operations and followed by a Gaussian filter to obtain the final decision map. Finally, the fused all-in-focus image is generated based on a weighted-sum strategy with the decision map. The experimental results demonstrate that the proposed method outperforms some state-of-the-art image fusion algorithms in terms of both qualitative and objective evaluations.
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“The data fusion in the anchor layers combines the data for further processing in the next layers [23].”
From Automated placental abruption identification using semantic segmentation, quantitative features, SVM, ensemble and multi-path CNN (2023) · cites this paper as [23]
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Cited by
Sorted by citations
Image fusion meets deep learning: A survey and perspective
Zhang, H., Xu, H., Tian, X. S., Jiang, J., & Ma, J. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.06.008 · Cited by 844
Multi-focus image fusion: A Survey of the state of the art
Liu, Y., Wang, L., Cheng, J., Li, C., & Chen, X. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.013 · Cited by 326
Classification Saliency-Based Rule for Visible and Infrared Image Fusion
Xu, H., Zhang, H., & Ma, J. (2021) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2021.3100986 · Cited by 297
Medical image fusion method by deep learning
Li, Y., Zhao, J., Lv, Z., & Li, J. (2021) · International Journal of Cognitive Computing in Engineering · doi:10.1016/j.ijcce.2020.12.004 · Cited by 280
Current advances and future perspectives of image fusion: A comprehensive review
Karim, S., Tong, G., Li, J., Qadir, A., Farooq, U., & Yu, Y. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.09.019 · Cited by 290
Metadata
- Authors
- Yang, Y., Nie, Z., Huang, S., Lin, P., & Wu, J.
- Affiliations
- Jiangxi University of Finance and Economics (CN)
- South Central Minzu University (CN)
- Venue
- IEEE Transactions on Computational Imaging
- Published
- 2019-06
- Volume
- 5
- Issue
- 2
- Pages
- 262-273
- ISSN
- 2333-9403 (ISSN-L)
- 2334-0118
- 2573-0436
- OpenAlex
- W2906859782
- Cited by
- 103
- References
- 41
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 16 · 2023: 12 · 2022: 16 · 2021: 30 · 2020: 11 · 2019: 5
- FWCI
- 10.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Computational Imaging (publishedVersion)
- Funders
- National Natural Science Foundation of China (61662026, 61862030, 61462031)
- Natural Science Foundation of Jiangxi Province (20182BCB22006, 20181BAB202010)
- Education Department of Jiangxi Province (KJLD14031, GJJ170312, GJJ170318)
- Topics
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Keywords
- multi-focus image fusion, morphological opening and closing, Gaussian filtering, decision map, weighted sum fusion, all-in-focus image
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