Authors:
Zhijie Zheng
1
;
2
;
3
;
Yuhang Jiao
4
and
Guangyou Fang
1
;
2
;
3
Affiliations:
1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
;
2
Key Laboratory of Electromagnetic Radiation and Sensing Technology, Chinese Academy of Sciences, Beijing, China
;
3
University of Chinese Academy of Sciences, Beijing, China
;
4
The University of Tokyo, Tokyo, Japan
Keyword(s):
Single Image Super-resolution, Convolutional Neural Network, Attention Mechanism.
Abstract:
Recently, convolutional neural network (CNN) has been widely used in single image super-resolution (SISR) and made significant advances. However, most of the existing CNN-based SISR models ignore fully utilization of the extracted features during upsampling, causing information bottlenecks, hence hindering the expressive ability of networks. To resolve these problems, we propose an upsampling attention network (UAN) for richer feature extraction and reconstruction. Specifically, we present a residual attention groups (RAGs) based structure to extract structural and frequency information, which is composed of several residual feature attention blocks (RFABs) with a non-local skip connection. Each RFAB adaptively rescales spatial- and channel-wise features by paying attention to correlations among them. Furthermore, we propose an upsampling attention block (UAB), which not only applies parallel upsampling processes to obtain richer feature representations, but also combines them to obt
ain better reconstruction results. Experiments on standard benchmarks show the advantage of our UAN over state-of-the-art methods both in objective metrics and visual qualities.
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