The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
DenseFuse: A Fusion Approach to Infrared and Visible Images
Open accessAbstract
In this paper, we present a novel deep learning architecture for infrared and visible images fusion problem. In contrast to conventional convolutional networks, our encoding network is combined by convolutional layers, fusion layer and dense block in which the output of each layer is connected to every other layer. We attempt to use this architecture to get more useful features from source images in encoding process. And two fusion layers(fusion strategies) are designed to fuse these features. Finally, the fused image is reconstructed by decoder. Compared with existing fusion methods, the proposed fusion method achieves state-of-the-art performance in objective and subjective assessment.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Multispectral pedestrian detection: Benchmark dataset and baseline
Hwang, S., Park, J., Kim, N., Choi, Y., & Kweon, I. S. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298706 · Cited by 1,249
Cited by
SDNet: A Versatile Squeeze-and-Decomposition Network for Real-Time Image Fusion
Zhang, H., & Ma, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01501-8 · Cited by 747
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
Zhao, Z., Bai, H., Zhu, Y., Zhang, J., Xu, S., Zhang, Y., Zhang, K., Meng, D., Timofte, R., & Van Gool, L. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00742 · Cited by 431
Dif-Fusion: Toward High Color Fidelity in Infrared and Visible Image Fusion With Diffusion Models
Yue, J., Fang, L., Xia, S., Deng, Y., & Ma, J. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3322046 · Cited by 304
DM-Fusion: Deep Model-Driven Network for Heterogeneous Image Fusion
Xu, G., He, C., Wang, H., Zhu, H., & Ding, W. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3238511 · Cited by 57
Metadata
- Authors
- Li, H., & Wu, X.-J.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2019-05
- Volume
- 28
- Issue
- 5
- Pages
- 2614-2623
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2798987894
- Cited by
- 2,032
- References
- 4
- FWCI
- 45.42
- Type
- journal article
- Language
- en
- Open access
- green
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