Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (8): 185-191.DOI: 10.3778/j.issn.1002-8331.1901-0331

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Image Super-Resolution Reconstruction Algorithm Based on Residual Network

CHEN Chen, LIU Mingming, LIU Bing, ZHOU Yong   

  1. 1. College of Computer  Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
    2. Institute of Intelligent Manufacturing, Jiangsu Vocational Institute of Architectural Technology, Xuzhou, Jiangsu 221008, China
    3. Aerospace Information Research Institute, Chinese Academy of Sciences, Xuzhou, Jiangsu 221116, China
  • Online:2020-04-15 Published:2020-04-14



  1. 1.中国矿业大学 计算机科学与技术学院,江苏 徐州 221116
    2.江苏建筑职业技术学院 智能制造学院,江苏 徐州 221008
    3.中国科学院 航空航天信息研究所,江苏 徐州 221116


The traditional convolutional neural network uses end-to-end mapping between high/low resolution images based on sparsely expressed super-resolution images. The input image is high resolution and the output image is low resolution. SRCNN with three convolution layers has a certain reconstruction effect, but the field of receptivity is low. Therefore, a deeper network structure is proposed. This improvement makes the latter network layer have a larger field of perception, so that the result pixels can be inferred from more pixels. However, considering the influence of network structure deepening on transmission rate, the learning rate is improved by introducing a combination of local residual learning and global residual learning. The convergence rate is effectively accelerated by this method. The experimental results are verified. Compared with existing Bicubic, SRCNN and VDSR, the reconstruction effect has been improved in peak signal-to-noise ratio, structural similarity and visual effect.

Key words: convolutional neural network, image super-resolution reconstruction, residual network



关键词: 卷积神经网络, 图像超分辨率重建, 残差网络