Row–Column Separated Attention Based Low‐Light Image/Video Enhancement
Loading...
Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
© 2024 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.
Abstract
U‐Net structure is widely used for low‐light image/video enhancement. The enhanced images result in areas with large local noise and loss of more details without proper guidance for global information. Attention mechanisms can better focus on and use global information. However, attention to images could significantly increase the number of parameters and computations. We propose a Row–Column Separated Attention module (RCSA) inserted after an improved U‐Net. The RCSA module's input is the mean and maximum of the row and column of the feature map, which utilizes global information to guide local information with fewer parameters. We propose two temporal loss functions to apply the method to low‐light video enhancement and maintain temporal consistency. Extensive experiments on the LOL, MIT Adobe FiveK image, and SDSD video datasets demonstrate the effectiveness of our approach.
Description
@article{10.1111:cgf.15192,
journal = {Computer Graphics Forum},
title = {{Row–Column Separated Attention Based Low‐Light Image/Video Enhancement}},
author = {Dong, Chengqi and Cao, Zhiyuan and Qi, Tuoshi and Wu, Kexin and Gao, Yixing and Tang, Fan},
year = {2024},
publisher = {© 2024 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.},
DOI = {10.1111/cgf.15192}
}