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Cited by 313

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 access
Yan, Qingsen, Gong, Dong, Shi, Qinfeng, van den Hengel, Anton, Shen, Chunhua, Reid, Ian, Zhang, Yanning·2019·2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Abstract

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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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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CoChat. (2026). Citation check: Attention-Guided Network for Ghost-Free High Dynamic Range Imaging. https://app.cochat.ai/papers/10.1109/cvpr.2019.00185
The paper
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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