Image Super-Resolution Reconstruction Based on Recursive Multi-scale Convolutional Networks

被引：0
|
作者：
Gao Q. [1 ]
Zhao J. [1 ]
Zhou Z. [1 ]
机构：
[1] Department of Applied Mathematics, College of Sciences, China Jiliang University, Hangzhou
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2020年 / 33卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Deep Learning; Multi-scale; Recursion; Super-Resolution Reconstruction;
D O I：
10.16451/j.cnki.issn1003-6059.202011002
中图分类号：
学科分类号：
摘要：
The performance of image super-resolution reconstruction networks is improved by deepening the depth.However, deepening the network makes the number of parameters increase rapidly, and thus it is hard to train the network and store the memory. To reduce the scale of the deep network and keep its reconstruction performance as much as possible, a concise recursive multi-scale convolutional network is proposed for super-resolution reconstruction based on the concepts of recursion and multi-scale. Firstly, the multi-scale module is employed to extract the features of the image with different scales. Then, the network is deepened by the recursive operation without increasing the number of network parameters. Finally, the outputs of each recursive operation are fused as the input for the reconstruction part. Experimental results show that the network parameters of the proposed method are fewer than those of some existing super-resolution methods with better reconstruction results. © 2020, Science Press. All right reserved.
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页码：972 / 980
页数：8
相关论文
共 26 条
  • [11] KIM J, LEE J K, LEE K M., Deeply-Recursive Convolutional Network for Image Super-Resolution, Proc of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1637-1645, (2016)
  • [12] TAI Y, YANG J, LIU X M., Image Super-Resolution via Deep Recursive Residual Network, Proc of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2790-2798, (2017)
  • [13] LI J C, FANG F M, MEI K F, Et al., Multi-scale Residual Network for Image Super-Resolution, Proc of the European Conference on Computer Vision, pp. 527-542, (2018)
  • [14] SHI W Z, CABALLERO J, HUSZAR F, Et al., Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-pixel Convolutional Neural Network, Proc of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874-1883, (2016)
  • [15] KINGMA D P, BA J L., ADAM: A Method for Stochastic Optimization
  • [16] TIMOFTE R, AGUSTSSON E, VAN GOOL L, Et al., NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results, Proc of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1110-1121, (2017)
  • [17] BEVILACQUA M, ROUMY A, GUILLEMOT C, Et al., Low-Com-plexity Single-Image Super-Resolution Based on Nonnegative Neigh-bor Embedding, Proc of the British Machine Vision Conference, pp. 1-13510, (2012)
  • [18] ZEYDE R, ELAD M, PROTTER M., On Single Image Scale-up Using Sparse-Representations, Proc of the International Confe-rence on Curves and Surfaces, pp. 711-730, (2010)
  • [19] ARBELAEZ P, MAIRE M, FOWLKES C, Et al., Contour Detection and Hierarchical Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 5, pp. 898-916, (2011)
  • [20] HUANG J B, SINGH A, AHUJA N., Single Image Super-Resolution from Transformed Self-exemplars, Proc of the IEEE Confe-rence on Computer Vision and Pattern Recognition, pp. 5197-5206, (2015)
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