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• Special Section of CVM 2020 •

### 基于渐进式通道注意力网络的参考图引导超分辨率研究

Huan-Jing Yue1, Member, IEEE, Sheng Shen1, Jing-Yu Yang1,*, Senior Member, IEEE, Hao-Feng Hu2, Yan-Fang Chen1

1. 1 School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China;
2 School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
• 收稿日期:2020-01-18 修回日期:2020-04-02 出版日期:2020-05-28 发布日期:2020-05-28
• 通讯作者: Jing-Yu Yang E-mail:yjy@tju.edu.cn
• 作者简介:Huan-Jing Yue received her B.S. and Ph.D. degrees in electronic and information engineering from Tianjin University, Tianjin, in 2010 and 2015, respectively. She was an intern with Microsoft Research Asia, Beijing, from 2011 to 2012, and from 2013 to 2015. She visited the Video Processing Laboratory, University of California at San Diego, from 2016 to 2017. She is currently an associate professor with the School of Electrical and Information Engineering, Tianjin University, Tianjin. Her current research interests include image processing and computer vision. She received the Microsoft Research Asia Fellowship Honor in 2013 and was selected into the Elite Scholar Program of Tianjin University in 2017.
• 基金资助:
This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61672378, 61771339, and 61520106002.

### Reference Image Guided Super-Resolution via Progressive Channel Attention Networks

Huan-Jing Yue1, Member, IEEE, Sheng Shen1, Jing-Yu Yang1,*, Senior Member, IEEE, Hao-Feng Hu2, Yan-Fang Chen1

1. 1 School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China;
2 School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
• Received:2020-01-18 Revised:2020-04-02 Online:2020-05-28 Published:2020-05-28
• Contact: Jing-Yu Yang E-mail:yjy@tju.edu.cn
• About author:Huan-Jing Yue received her B.S. and Ph.D. degrees in electronic and information engineering from Tianjin University, Tianjin, in 2010 and 2015, respectively. She was an intern with Microsoft Research Asia, Beijing, from 2011 to 2012, and from 2013 to 2015. She visited the Video Processing Laboratory, University of California at San Diego, from 2016 to 2017. She is currently an associate professor with the School of Electrical and Information Engineering, Tianjin University, Tianjin. Her current research interests include image processing and computer vision. She received the Microsoft Research Asia Fellowship Honor in 2013 and was selected into the Elite Scholar Program of Tianjin University in 2017.
• Supported by:
This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61672378, 61771339, and 61520106002.

Abstract: In recent years, the convolutional neural networks (CNNs) for single image super-resolution (SISR) are becoming more and more complex, and it is more challenging to improve the SISR performance. In contrast, the reference image guided super-resolution (RefSR) is an effective strategy to boost the SR (super-resolution) performance. In RefSR, the introduced high-resolution (HR) references can facilitate the high-frequency residual prediction process. According to the best of our knowledge, the existing CNN-based RefSR methods treat the features from the references and the low-resolution (LR) input equally by simply concatenating them together. However, the HR references and the LR inputs contribute differently to the final SR results. Therefore, we propose a progressive channel attention network (PCANet) for RefSR. There are two technical contributions in this paper. First, we propose a novel channel attention module (CAM), which estimates the channel weighting parameter by weightedly averaging the spatial features instead of using global averaging. Second, considering that the residual prediction process can be improved when the LR input is enriched with more details, we perform super-resolution progressively, which can take advantage of the reference images in multi-scales. Extensive quantitative and qualitative evaluations on three benchmark datasets, which represent three typical scenarios for RefSR, demonstrate that our method is superior to the state-of-the-art SISR and RefSR methods in terms of PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity).

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