Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 4
1271-1276

Image super-resolution based on depth residual back projection attention network

Hu Gaopenga
Chen Ziliua
Wang Xiaominga,b
Zhang Kaifanga
Huang Zengxia
Du Yajuna
a. School of Computer & Software Engineering, b. Robotics Research Center, Xihua University, Chengdu 610039, China
DOI: 10.19734/j.issn.1001-3695.2020.03.0081

Abstract

Focused on the partly issue that in the process of single-frame image super-resolution reconstruction, such as insufficient utilization of feature information during image super-resolution reconstruction, the interdependence between the channels of the feature map is difficult to determine, and reconstruction errors existing at high-resolution image reconstructed, this paper proposed a single image super resolution method based on depth residual back projection attention network. It used the residual learning to ease the training difficulty and fully discover the feature information of the image, and used the back-projection method to learn the interdependence between the high-resolution and low-resolution images. In addition, it introduced the attention mechanism to assign each feature map with different attention to discover more high-frequency information, and learnt the interdependence between the channels of the feature maps. The experimental results show that compared with most single-frame image super-resolution methods, the proposed method not only has a significant improvement in objective indicators, but also the reconstructed predicted image has richer texture information.

Foundation Support

国家自然科学基金资助项目(61602390)
西华大学研究生创新基金资助项目(ycjj2019095)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.03.0081
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 4
Section: Technology of Graphic & Image
Pages: 1271-1276
Serial Number: 1001-3695(2021)04-060-1271-06

Publish History

[2021-04-05] Printed Article

Cite This Article

胡高鹏, 陈子鎏, 王晓明, 等. 基于深度残差反投影注意力网络的图像超分辨率 [J]. 计算机应用研究, 2021, 38 (4): 1271-1276. (Hu Gaopeng, Chen Ziliu, Wang Xiaoming, et al. Image super-resolution based on depth residual back projection attention network [J]. Application Research of Computers, 2021, 38 (4): 1271-1276. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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