Chinese Journal of Liquid Crystals and Displays, Volume. 36, Issue 12, 1720(2021)

Image super resolution reconstruction algorithm based on generative countermeasure network

LIU Guo-qi*, LIU Jin-feng, and ZHU Dong-hui
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    References(13)

    [3] [3] JADERBERG M, SIMONYAN K, ZISSERMAN A. Spatial transformer networks [C]//Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal: ACM, 2015: 2017-2025.

    [4] [4] IRANI M, PELEG S. Super resolution from image sequences [C]//10th International Conference on Pattern Recognition. Atlantic City: IEEE, 1990: 115-120.

    [5] [5] STARK H, OSKOUI P. High-resolution image recovery from image-plane arrays, using convex projections [J]. Journal of the Optical Society of America A, 1989, 6(11): 1715-1726.

    [8] [8] DONG C, LOY C C, HE K M, et al. Learning a deep convolutional network for image super-resolution [C]//13th European Conference on Computer Vision. Zurich: Springer, 2014: 184-199.

    [9] [9] KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks [C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 1646-1654.

    [10] [10] 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: 448-456.

    [11] [11] LIM B, SON S, KIM H, et al. Enhanced deep residual networks for single image super-resolution [C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu: IEEE, 2017: 1132-1140.

    [12] [12] LAI W S, HUANG J B, AHUJA N, et al. Deep Laplacian pyramid networks for fast and accurate super-resolution [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 5835-5843.

    [13] [13] ZHANG Y L, TIAN Y P, KONG Y, et al. Residual dense network for image super-resolution [C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 2472-2481.

    [14] [14] LEDIG C, THEIS L, HUSZR F, et al. Photo-realistic single image super-resolution using a generative adversarial network [C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 105-114.

    [15] [15] XU X W, WANG J W, ZHONG B F, et al. Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism [J]. Measurement, 2021, 177: 109254.

    [16] [16] WANG Q L, WU B G, ZHU P F, et al. ECA-Net: efficient channel attention for deep convolutional neural networks [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020: 11531-11539.

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    LIU Guo-qi, LIU Jin-feng, ZHU Dong-hui. Image super resolution reconstruction algorithm based on generative countermeasure network[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(12): 1720

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    Paper Information

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    Received: Sep. 1, 2021

    Accepted: --

    Published Online: Jan. 1, 2022

    The Author Email: LIU Guo-qi (474313871@qq.com)

    DOI:10.37188/cjlcd.2021-0227

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