The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
IFCNN: A general image fusion framework based on convolutional neural network
Abstract
Abstract In this paper, we propose a general image fusion framework based on the convolutional neural network, named as IFCNN. Inspired by the transform-domain image fusion algorithms, we firstly utilize two convolutional layers to extract the salient image features from multiple input images. Afterwards, the convolutional features of multiple input images are fused by an appropriate fusion rule (elementwise-max, elementwise-min or elementwise-mean), which is selected according to the type of input images. Finally, the fused features are reconstructed by two convolutional layers to produce the informative fusion image. The proposed model is fully convolutional, so it could be trained in the end-to-end manner without any post-processing procedures. In order to fully train the model, we have generated a large-scale multi-focus image dataset based on the large-scale RGB-D dataset (i.e., NYU-D2), which owns ground-truth fusion images and contains more diverse and larger images than the existing datasets for image fusion. Without finetuning on other types of image datasets, the experimental results show that the proposed model demonstrates better generalization ability than the existing image fusion models for fusing various types of images, such as multi-focus, infrared-visual, multi-modal medical and multi-exposure images. Moreover, the results also verify that our model has achieved comparable or even better results compared to the state-of-the-art image fusion algorithms on four types of image datasets.
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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
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913
The Laplacian pyramid as a compact image code
Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications · doi:10.1109/tcom.1983.1095851 · Cited by 6,077
Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760
Cited by
Azam, M. A., Khan, K. B., Salahuddin, S., Rehman, E., Khan, S. A., Khan, M. A., Kadry, S., & Gandomi, A. H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105253 · Cited by 449
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
Self-citationDeep learning based multimodal biomedical data fusion: an overview and comparative review
Duan, J., Xiong, J., Li, Y., & Ding, W. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102536 · Cited by 284
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
Xu, S., Ji, L., Wang, Z., Li, P., Sun, K., Zhang, C., & Zhang, J. (2020) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2020.3039564 · Cited by 47
Degradation-Resistant Unfolding Network for Heterogeneous Image Fusion
He, C., Li, K., Xu, G., Zhang, Y., Hu, R., Guo, Z., & Li, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01159 · Cited by 37
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Metadata
- Authors
- Zhang, Y., Liu, Y., Sun, P., Yan, H., Zhao, X., & Zhang, L.
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 54
- Pages
- 99-118
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W2963787388
- Cited by
- 1,550
- References
- 7
- FWCI
- 58.06
- Type
- journal article
- Language
- en
- Open access
- closed
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