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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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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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