Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 7
2235-2240

Single-image super-resolution network based on dual learning strategy

Chen Jinling1
Peng Yanbing2
Li Nian2
1. Wuhan Research Institute of Posts & Telecommunications, Wuhan 430074, China
2. Nanjing Fiberhome Tiandi Communication Technology Co. , Ltd. , Nanjing 210019, China
DOI: 10.19734/j.issn.1001-3695.2020.07.0267

Abstract

Aiming at the ill-posedness of the single-image low-resolution to super-resolution mapping, the low utilization of feature space information by the super-resolution reconstruction network, and the excessive amount of network parameters, this paper proposed a dual learning algorithm based on progressive up-sampling, which was applied to super-resolution reconstruction of a single image. The algorithm adopted deep separable convolution to significantly reduced the amount of network parameters, and constructed the progressive up-sampling network based on sub-pixel convolution to efficiently use the spatial information of the feature image. Meanwhile, it used dual learning to construct a closed-loop feedback connection network to obtain the optimal mapping function to estimate the down-sampling kernel to reconstruct low-resolution images. Analyzing on benchmark datasets such as Set5, Set14, BSDS100, Urban100, and Manga109 compared with the state-of-the-arts models, this algorithm can reduce the number of parameters by 9%, and can effectively alleviate the image edge distortion and artifact phenomenon under the large factors, the average PSRN/SSIM are 26.90/0.751、24.84/0.645、24.74/0.619、22.30/0.560、24.38/0.706.

Foundation Support

国家重点研发计划资助项目(2017YFB1400704)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.07.0267
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 7
Section: Technology of Graphic & Image
Pages: 2235-2240
Serial Number: 1001-3695(2021)07-060-2235-06

Publish History

[2021-07-05] Printed Article

Cite This Article

陈金玲, 彭艳兵, 李念. 基于对偶学习策略的单图像超分辨率重建网络 [J]. 计算机应用研究, 2021, 38 (7): 2235-2240. (Chen Jinling, Peng Yanbing, Li Nian. Single-image super-resolution network based on dual learning strategy [J]. Application Research of Computers, 2021, 38 (7): 2235-2240. )

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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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