Chinese Journal of Liquid Crystals and Displays, Volume. 36, Issue 5, 705(2021)

Single frame image super-resolution reconstruction based on improved generative adversarial network

CHEN Zong-hang1, HU Hai-long1, YAO Jian-min1,2、*, YAN Qun1,2, and LIN Zhi-xian1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • show less
    References(19)

    [3] [3] DUCHON C E. Lanczos filtering in one and two dimensions[J]. Journal of Applied Meteorology and Climatology, 1979, 18(8): 1016-1022.

    [4] [4] HASAN M S, HAQUE S T. Single image super-resolution using back-propagation neural networks[C]//Proceedings of 2017 20th International Conference of Computer and Information Technology. Dhaka: IEEE, 2017.

    [7] [7] DONG C, LOY C C, HE K M, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 295-307.

    [8] [8] KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016.

    [9] [9] LAI W S, HUANG J B, AHUJA N, et al. Fast and accurate image super-resolution with deep laplacian pyramid networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(11): 2599-2613.

    [10] [10] TANG Y L, GONG W G,CHEN X, et al. Deep inception-residual laplacian pyramid networks for accurate single-image super-resolution[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(5): 1514-1528.

    [11] [11] LEDIG C, THEIS L, HUSZR F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//Proceedings of 2017 Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017.

    [12] [12] GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks[C]//Advances in Neural Information Processing Systems. 2014: 2672-2680.

    [13] [13] ZHAO H,GALLO O, FROSIO I, et al. Loss functions for image restoration with neural networks[J]. IEEE Transactions on Computational Imaging, 2017, 3(1): 47-57.

    [14] [14] ARJOVSKY M, CHINTALA S, BOTTOU L. Wasserstein GAN[J]. arXiv preprint arXiv, 1701.07875, 2017.

    [16] [16] HE K M, ZHANG X Y,REN S Q, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016.

    [17] [17] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017.

    [18] [18] SHI W Z,CABALLERO J, HUSZR F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016.

    [19] [19] LIM B, SON S, KIM H, et al. Enhanced deep residual networks for single image super-resolution[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu: IEEE, 2017.

    [20] [20] IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 32nd International Conference on International Conference on Machine Learning. Lille: ACM, 2015.

    [21] [21] SHAMIR O. Discussion of: Nonparametric regression using deep neural networks with ReLU activation function[J]. The Annals of Statistics, 2020, 48(4): 1911-1915.

    [22] [22] ARJOVSKY M, BOTTOU L. Towards principled methods for training generative adversarial networks[J]. arXiv preprint arXiv, 1701.04862, 2017.

    CLP Journals

    [1] LI De-cai, YAN Qun, YAO Jian-min, LIN Zhi-xian, DONG Ze-yu. Video inpainting based on residual convolution attention network[J]. Chinese Journal of Liquid Crystals and Displays, 2022, 37(1): 86

    [2] YANG Yun, ZHOU Shu-jie, LI Cheng-hui, ZHANG Juan-juan. Retinal image segmentation method based on dense cycle networks[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(12): 1702

    Tools

    Get Citation

    Copy Citation Text

    CHEN Zong-hang, HU Hai-long, YAO Jian-min, YAN Qun, LIN Zhi-xian. Single frame image super-resolution reconstruction based on improved generative adversarial network[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(5): 705

    Download Citation

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

    Category:

    Received: Sep. 24, 2020

    Accepted: --

    Published Online: Aug. 26, 2021

    The Author Email: YAO Jian-min (yaojm@fzu.edu.cn)

    DOI:10.37188/cjlcd.2020-0250

    Topics