Abstract
We introduce DRHDR, a Dual branch Residual Convolutional Neural Network for Multi-Bracket HDR Imaging. To address the challenges of fusing multiple brackets from dynamic scenes, we propose an efficient dual branch network that operates on two different resolutions. The full resolution branch uses a Deformable Convolutional Block to align features and retain high-frequency details. A low resolution branch with a Spatial Attention Block aims to attend wanted areas from the non-reference brackets, and suppress displaced features that could incur on ghosting artifacts. By using a dual branch approach we are able to achieve high quality results while constraining the computational resources required to estimate the HDR results.
| Original language | English |
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| Journal | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
| Pages (from-to) | 843-851 |
| Number of pages | 9 |
| ISSN | 2160-7508 |
| DOIs | |
| Publication status | Published - 2022 |
| Event | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 - New Orleans, United States Duration: 19. Jun 2022 → 20. Jun 2022 |
Conference
| Conference | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 |
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| Country/Territory | United States |
| City | New Orleans |
| Period | 19/06/2022 → 20/06/2022 |
Bibliographical note
Funding Information:This work was supported by the Industrial Ph.D. program’s financial support from Innovation Fund Denmark, through the project AIERE (Contract-No: 9065-00099B).