Gamma-Enhanced Spatial Attention Network for Efficient High Dynamic Range Imaging

Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 1032-1040

Abstract


High dynamic range(HDR) imaging is the task of recovering HDR image from one or multiple input Low Dynamic Range (LDR) images. In this paper, we present Gamma-enhanced Spatial Attention Network(GSANet), a novel framework for reconstructing HDR images. This problem comprises two intractable challenges of how to tackle overexposed and underexposed regions and how to overcome the paradox of performance and complexity trade-off. To address the former, after applying gamma correction on the LDR images, we adopt a spatial attention module to adaptively select the most appropriate regions of various exposure low dynamic range images for fusion. For the latter one, we propose an efficient channel attention module, which only involves a handful of parameters while bringing clear performance gain. Experimental results show that the proposed method achieves better visual quality on the HDR dataset. The code will be available at:https://github.com/fancyicookie/GSANet.

Related Material


[pdf]
[bibtex]
@InProceedings{Li_2022_CVPR, author = {Li, Fangya and Gang, Ruipeng and Li, Chenghua and Li, Jinjing and Ma, Sai and Liu, Chenming and Cao, Yizhen}, title = {Gamma-Enhanced Spatial Attention Network for Efficient High Dynamic Range Imaging}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {1032-1040} }