.png){kind=icon}
返回
Prior embedding multi-degradations super resolution network
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2021.12.066.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Nowadays, multi-degradations super-resolution (MDSR) has gradually received wide attention for its ability to deal with multiple degradations including blur, noise in a low-resolution (LR) image. Deep neural networks based MDSR methods have gained rapid development by utilizing the degradation prior. Though achieving promising results, most existing MDSR models fail to fully utilize the degradation prior and efficiently reconstruct high-resolution (HR) image by simple concatenation and iterative operations. This paper proposes an end-to-end MDSR approach to effectively extract features from the degradation prior and embed the degradation features into image features via the degradation feature extraction module and the prior embedding module. In addition, the axis attention mechanism and the pixel attention mechanism are proposed to augment the representation power of image features. Extensive experiments demonstrate that our approach can simultaneously solve different degradations and produce state-of-the-art results on standard benchmark datasets, with advantages in terms of quantitative metrics, visual quality, and reconstruction efficiency.(c) 2021 Published by Elsevier B.V.
Keyword:
Single image super resolution
Multi degradations
Axis attention and pixel attention
mechanisms
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Caw’s Walking State Recognition Based on Accelerometers and Gyroscopes Installed on Ear-Tags and Collar-Tags基于安装在耳标和项圈上的加速度计和陀螺仪的Caw步行状态识别
.png){kind=icon}
PREAI
分享收藏分享收藏分享收藏
查看更多内容