ADNet: Attention-Guided Deformable Convolutional Network for High Dynamic Range Imaging
Abstract
In this paper, we present an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet. This problem comprises two intractable challenges of how to handle saturation and noise properly and how to tackle misalignments caused by object motion or camera jittering. To address the former, we adopt a spatial attention module to adaptively select the most appropriate regions of various expo-sure low dynamic range (LDR) images for fusion. For the latter one, we propose to align the gamma-corrected images in the feature-level with a Pyramid, Cascading and Deformable (PCD) alignment module. The proposed AD-Net shows state-of-the-art performance compared with previous methods, achieving a PSNR-l of 39.4471 and a PSNR-μ of 37.6359 in NTIRE 2021 Multi-Frame HDR Challenge.
Cite
Text
Liu et al. "ADNet: Attention-Guided Deformable Convolutional Network for High Dynamic Range Imaging." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021. doi:10.1109/CVPRW53098.2021.00057Markdown
[Liu et al. "ADNet: Attention-Guided Deformable Convolutional Network for High Dynamic Range Imaging." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021.](https://mlanthology.org/cvprw/2021/liu2021cvprw-adnet/) doi:10.1109/CVPRW53098.2021.00057BibTeX
@inproceedings{liu2021cvprw-adnet,
title = {{ADNet: Attention-Guided Deformable Convolutional Network for High Dynamic Range Imaging}},
author = {Liu, Zhen and Lin, Wenjie and Li, Xinpeng and Rao, Qing and Jiang, Ting and Han, Mingyan and Fan, Haoqiang and Sun, Jian and Liu, Shuaicheng},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2021},
pages = {463-470},
doi = {10.1109/CVPRW53098.2021.00057},
url = {https://mlanthology.org/cvprw/2021/liu2021cvprw-adnet/}
}