Laser & Optoelectronics Progress, Volume. 54, Issue 11, 111005(2017)

Super-Resolution Reconstruction of Image Based on Optimized Convolution Neural Network

Wang Min, Liu Kexin*, Liu Li, and Yang Runling
Author Affiliations
  • [in Chinese]
  • show less
    References(23)

    [2] [2] Yang Aiping, Bai Huanghuang. Nighttime image defogging based on the theory of Retinex and dark channel prior[J]. Laser & Optoelectronics Progress, 2017, 54(4): 041002.

    [3] [3] Su Heng, Zhou Jie, Zhang Zhihao. Survey of super-resolution image reconstruction methods[J]. Acta Automatica Sinica, 2013, 39(8): 1202-1213.

    [4] [4] Chen Jian, Gao Huibin, Wang Weiguo, et al. Methods and applications of image super-resolution restoration[J]. Laser & Optoelectronics Progress, 2015, 52(2): 020004.

    [5] [5] Zhou Jinghong, Zhou Cui, Zhu Jianjun, et al. A method of super-resolution reconstruction for remote sensing image based on non-subsampled contourlet transform[J]. Acta Optica Sinica, 2015, 35(1): 0110001.

    [7] [7] Dong C, Loy C G, He K, et al. Learning a deep convolutional network for image super-resolution[C]. European Conference on Computer Vision, 2014: 184-199.

    [8] [8] Yang J C, Wright J, Huang T S, et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 2010, 19(11): 2861-2873.

    [9] [9] Timofte R, de Smet V, van Gool L. A+: adjusted anchored neighborhood regression for fast super-resolution[C]. Asian Conference on Computer Vision, 2014: 111-126.

    [10] [10] Yu D, Deng L. Deep learning and its applications to signal and information processing [exploratory dsp][J]. IEEE Signal Processing Magazine, 2011, 28(1): 145-154.

    [11] [11] Gan C, Wang N, Yang Y, et al. DevNet: a deep event network for multimedia event detection and evidence recounting[C].Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2015: 2568-2577.

    [12] [12] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition [EB/OL]. [2017-01-16]. https://arxiv.org/abs/1409.1556.

    [13] [13] He K, Sun J. Convolutional neural networks at constrained time cost[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2015: 5353-5360.

    [14] [14] Zhang Baochang, Yang Wankou, Lin Nana. Machine learning and visual perception[M]. Beijing: Tsinghua University Press, 2016.

    [15] [15] Nair V, Hinton G E. Rectified linear units improve restricted Boltzmann machines[C]. Proceedings of 27th International Conference on Machine Learning, 2010: 807-814.

    [16] [16] Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.

    [17] [17] Li Yandong, Hao Zongbo, Lei Hang. Survey of convolutional neural network[J]. Journal of Computer Applications, 2016, 36(9): 2508-2514.

    [18] [18] Bouvrie J. Note on convolutional neural networks[EB/OL].[2016-11-27]. http//cogprints.org/5869/1/c-nn_tutorial.pdf.

    [19] [19] Fu X L, Cai L H, Liu Y, et al. A computational cognition model of perception, memory, and judgment[J]. Science China-Information Sciences, 2014, 57(3): 032114.

    [21] [21] Zhao Zhigang, Lin Yujiao, Yin Zhaoyuan. A mean particle swarm optimization algorithm based on adaptive inertia weight[J]. Computer Engineering & Science, 2016, 38(3): 501-506.

    [22] [22] Lu Yuxuan. Image super-resolution using convolutional networks and its visual improvement[D]. Hefei: Anhui University, 2016: 5-27.

    [23] [23] Liu L X, Liu B, Huang H, et al. No-reference image quality assessment based on spatial and spectral entropies[J]. Signal Processing Image Communication, 2014, 29(8): 856-863.

    CLP Journals

    [1] Can Wang, Fan Yang, Jing Li. Blind Recovery Method of Motion Blurred Image Based on Combining l1/l2 Norm with High Order and Low Order Total Variation[J]. Laser & Optoelectronics Progress, 2018, 55(4): 041015

    [2] Guang Miao, Chaofeng Li. Detection of Pulmonary Nodules CT Images Combined with Two-Dimensional and Three-Dimensional Convolution Neural Networks[J]. Laser & Optoelectronics Progress, 2018, 55(5): 051006

    Tools

    Get Citation

    Copy Citation Text

    Wang Min, Liu Kexin, Liu Li, Yang Runling. Super-Resolution Reconstruction of Image Based on Optimized Convolution Neural Network[J]. Laser & Optoelectronics Progress, 2017, 54(11): 111005

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Image Processing

    Received: May. 25, 2017

    Accepted: --

    Published Online: Nov. 17, 2017

    The Author Email: Kexin Liu (m18602907837@163.com)

    DOI:10.3788/lop54.111005

    Topics