The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Attention-Guided Network for Ghost-Free High Dynamic Range Imaging
Open accessAbstract
Ghosting artifacts caused by moving objects or misalignments is a key challenge in high dynamic range (HDR) imaging for dynamic scenes. Previous methods first register the input low dynamic range (LDR) images using optical flow before merging them, which are error-prone and cause ghosts in results. A very recent work tries to bypass optical flows via a deep network with skip-connections, however, which still suffers from ghosting artifacts for severe movement. To avoid the ghosting from the source, we propose a novel attention-guided end-to-end deep neural network (AHDRNet) to produce high-quality ghost-free HDR images. Unlike previous methods directly stacking the LDR images or features for merging, we use attention modules to guide the merging according to the reference image. The attention modules automatically suppress undesired components caused by misalignments and saturation and enhance desirable fine details in the non-reference images. In addition to the attention model, we use dilated residual dense block (DRDB) to make full use of the hierarchical features and increase the receptive field for hallucinating the missing details. The proposed AHDRNet is a non-flow-based method, which can also avoid the artifacts generated by optical-flow estimation error. Experiments on different datasets show that the proposed AHDRNet can achieve state-of-the-art quantitative and qualitative results.
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Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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
(2010) · Cited by 14,134
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
(2017) · Cited by 11,625
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
Residual Dense Network for Image Super-Resolution
Zhang, Y., Tian, Y., Kong, Y., Zhong, B., & Fu, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00262 · Cited by 4,168
Self-citationLoss Functions for Image Restoration With Neural Networks
Zhao, H., Gallo, O., Frosio, I., & Kautz, J. (2017) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2016.2644865 · Cited by 2,641
Recovering high dynamic range radiance maps from photographs
Debevec, P. E., & Malik, J. (2008) · ACM SIGGRAPH 2008 classes · doi:10.1145/1401132.1401174 · Cited by 2,399
Recovering high dynamic range radiance maps from photographs
Debevec, P. E., & Malik, J. (1997) · Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97 · doi:10.1145/258734.258884 · Cited by 1,775
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Self-citationLiu, J., Liu, Z., Wu, G., Ma, L., Liu, R., Zhong, W., Luo, Z., & Fan, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00745 · Cited by 316
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Metadata
- Authors
- Yan, Q., Gong, D., Shi, Q., van den Hengel, A., Shen, C., Reid, I., & Zhang, Y.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 1751-1760
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963728414
- Cited by
- 313
- References
- 41
- FWCI
- 9.95
- Type
- conference paper
- Language
- en
- Open access
- green
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- Yan, Q., Gong, D., Shi, Q., van den Hengel, A., Shen, C., Reid, I., & Zhang, Y. (2019). Attention-Guided Network for Ghost-Free High Dynamic Range Imaging. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00185
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