Chinese Journal of Liquid Crystals and Displays, Volume. 36, Issue 2, 317(2021)

Image super-resolution reconstruction based on wavelet domain

DONG Ben-zhi*, YU Ming-cong, and ZHAO Peng
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    References(27)

    [1] [1] PARK S C, PARK M K, KANG M G. Super-resolution image reconstruction: a technical overview [J]. IEEE Signal Processing Magazine, 2003, 20(3): 21-36.

    [2] [2] HARRIS J L. Diffraction and resolving power [J].Journal of the Optical Society of America, 1964, 54(7): 931-936.

    [9] [9] 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.

    [10] [10] DONG C, LOY C C, TANG X O. Accelerating the super-resolution convolutional neural network [C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016: 391-407.

    [11] [11] 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 [C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016: 1874-1883.

    [12] [12] 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, NV, USA: IEEE, 2016: 770-778.

    [13] [13] 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, NV, USA: IEEE, 2016: 1646-1654.

    [14] [14] KIM J, LEE J K, LEE K M. Deeply-recursive convolutional network for image super-resolution [C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016: 1637-1645.

    [15] [15] TAI Y, YANG J, LIU X M. Image super-resolution via deep recursive residual network [C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017: 2790-2798.

    [16] [16] LAI W S, HUANG J B, AHUJA N, et al. Deep Laplacian pyramid networks for fast and accurate super-resolution [C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017: 5835-5843.

    [17] [17] LEDIG C, THEIS L, HUSZR F, et al. Photo-realistic single image super-resolution using a generative adversarial network [C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017: 105-114.

    [18] [18] 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, HI, USA: IEEE, 2017: 2261-2269.

    [19] [19] LI S M, FAN R, LEI G Q, et al. A two-channel convolutional neural network for image super-resolution [J]. Neurocomputing, 201, 2758: 267-277.

    [20] [20] DU X F, QU X B, HE Y F, et al. Single image super-resolution based on multi-scale competitive convolutional neural network [J]. Sensors, 2018, 18(3): 789.

    [21] [21] BAE W, YOO J, YE J C. Beyond deep residual learning for image restoration: persistent homology-guided manifold simplification [C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu, HI, USA: IEEE, 2017: 1141-1149.

    [22] [22] GUO T T, MOUSAVI H S, VU T H, et al. Deep wavelet prediction for image super-resolution [C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu, HI, USA: IEEE, 2017: 1100-1109.

    [23] [23] LIU P J, ZHANG H Z, ZHANG K, et al. Multi-level wavelet-CNN for image restoration [C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City, UT, USA: IEEE, 2018.

    [24] [24] RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation [C]//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer, 2015: 234-241.

    [25] [25] YU X, PORIKLI F. Ultra-resolving face images by discriminative generative networks [C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016: 318-333.

    [26] [26] MALLAT S. Wavelets for a vision[J].Proceedings of the IEEE, 1996, 84(4): 604-614, doi: 10.1109/5.488702.

    [27] [27] HUANG H B, HE R, SUN Z A, et al. Wavelet domain generative adversarial network for multi-scale face hallucination [J]. International Journal of Computer Vision, 2019, 127(6/7): 763-784.

    [28] [28] TIMOFTE R, GU S H, WU J Q, et al. NTIRE 2018 challenge on single image super-resolution: methods and results [C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City, UT, USA: IEEE, 2018.

    [29] [29] BEVILACQUA M, ROUMY A, GUILLEMOT C, et al. Low-complexity single-image super-resolution based on nonnegative neighbor embedding [C]//Proceedings of the 23rd British Machine Vision Conference. Surrey, United Kingdom: BMVA Press, 2012: 1-10.

    [30] [30] ZEYDE R, ELAD M, PROTTER M. On single image scale-up using sparse-representations [C]//Proceedings of the 7th International Conference on Curves and Surfaces. Avignon, France: Springer, 2010: 711-730.

    [31] [31] MARTIN D, FOWLKES C, TAL D, et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics [C]//Proceedings of the Eighth IEEE International Conference on Computer Vision. Vancouver, BC, Canada: IEEE, 2001.

    [32] [32] HUANG J B, SINGH A, AHUJA N. Single image super-resolution from transformed self-exemplars [C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE, 2015: 5197-5206.

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    DONG Ben-zhi, YU Ming-cong, ZHAO Peng. Image super-resolution reconstruction based on wavelet domain[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(2): 317

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    Paper Information

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    Received: Jun. 28, 2020

    Accepted: --

    Published Online: Mar. 30, 2021

    The Author Email: DONG Ben-zhi (nefu_dbz@163.com)

    DOI:10.37188/cjlcd.2020-0101

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