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3978 Journal of Software 软件学报 Vol.31, No.12, December 2020

[9] Yang J, Wright J, Huang TS, Ma Y. Image super-resolution via sparse representation. IEEE Trans. on Image Processing (TIP),
2010,19(11):2861−2873. [doi: 10.1109/TIP.2010.2050625]
[10] Dong C, Loy CC, He KM, Tang XO. Image super-resolution using deep convolutional networks. IEEE Trans. on Pattern Analysis
and Machine Intelligence (T-PAMI), 2016,38(2):295−307. [doi: 10.1109/TPAMI.2015.2439281]
[11] Wang ZY, Liu D, Chang SY, Ling Q, Yang YZ, Huang TS. D3: Deep dual-domain based fast restoration of JPEG-compressed
image. In: Proc. of the IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR). 2016. 2764−2772. [doi: 10.1109/
CVPR.2016.302]
[12] Lee K, Kim DS, Kim T. Regression-Based prediction for blocking artifact reduction in jpeg-compressed images. IEEE Trans. on
Image Processing (TIP), 2005,14(1):36−48. [doi: 10.1109/TIP.2004.838699]
[13] Chang H, Ng MK, Zeng T. Reducing artifacts in JPEG decompression via a learned dictionary. IEEE Trans. on Signal Processing
(TSP), 2014,62(3):718−728. [doi: 10.1109/TSP.2013.2290508]
[14] Dong C, Loy CC, He KM, Tang XO. Learning a deep convolutional network for image super-resolution. In: Proc. of the European
Conf. on Computer Vision (ECCV). 2014. 184−199. [doi: 10.1007/978-3-319-10593-2_13]
[15] Zhang YL, Li KP, Li K, Wang LC, Zhong BN, Fu Y. Image super-resolution using very deep residual channel attention networks.
In: Proc. of the European Conf. on Computer Vision (ECCV). 2018. 294−310. [doi: 10.1007/978-3-030-01234-2_18]
[16] Wang XT, Yu K, Dong C, Loy CC. Recovering realistic texture in image super-resolution by deep spatial feature transform. In:
Proc. of the IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR). 2018. 606−615. [doi: 10.1109/CVPR.2018.
00070]
[17] Ledig C, Theis L, Huszar F, Caballero J, Cunningham A. Photo-Realistic single image super-resolution using a generative
adversarial network. In: Proc. of the IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR). 2017. 105−114. [doi:
10.1109/CVPR.2017.19]
[18] Galteri L, Seidenari L, Bertini M, Bimbo AD. Deep generative adversarial compression artifact removal. In: Proc. of the IEEE Int’l
Conf. on Computer Vision (ICCV). 2017. 4836−4845. [doi: 10.1109/ICCV.2017.517]
[19] Guo J, Chao HY. One-to-Many network for visually pleasing compression artifacts reduction. In: Proc. of the IEEE Int’l Conf. on
Computer Vision and Pattern Recognition (CVPR). 2017. 4867−4876. [doi: 10.1109/CVPR.2017.517]
[20] Goodfellow I, Jean PA, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Proc. of
the Advances in Neural Information Processing Systems (NIPS). 2014. 2672−2680.
[21] Guo T, Mousavi HS, Vu TH, Monga V. Deep wavelet prediction for image super-resolution. In: Proc. of the IEEE Int’l Conf. on
Computer Vision and Pattern Recognition Workshops (CVPRW). 2017. 1100−1109. [doi: 10.1109/CVPRW.2017.148]
[22] Huang HB, He R, Sun ZN, Tan TN. Wavelet-SRNET: A wavelet-based CNN for multi-scale face super resolution. In: Proc. of the
IEEE Int’l Conf. on Computer Vision (ICCV). 2017. 1698−1706. [doi: 10.1109/ICCV.2017.187]
[23] Liu PJ, Zhang HZ, Zhang K, Lin L, Zuo WM. Multi-Level wavelet-CNN for image restoration. In: Proc. of the IEEE Int’l Conf. on
Computer Vision and Pattern Recognition Workshops (CVPRW). 2018. 886−895. [doi: 10.1109/CVPRW.2018.00121]
[24] Do MN, Vetterli M. The contourlet transform: An efficient directional multiresolution image representation. IEEE Trans. on Image
Processing (TIP), 2005,14(12):2091−2106. [doi: 10.1109/TIP.2005.859376]
[25] Shi WZ, Caballero J, Huszar F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z. Real-Time single image and video super-
resolution using an efficient sub-pixel convolutional neural network. In: Proc. of the IEEE Int’l Conf. on Computer Vision and
Pattern Recognition (CVPR). 2016. 1874−1883. [doi: 10.1109/CVPR.2016.207]
[26] Peng YL, Zhang L, Zhang Y, Liu SG, Guo M. Deep deconvolution neural network for image super-resolution. Ruan Jian Xue Bao/
Journal of Software, 2018,29(4):926−934 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5407.htm [doi:
10.13328/j.cnki.jos.005407]
[27] Lai WS, Huang JB, Ahuja N, Yang MH. Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. of the
IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR). 2017. 5835−5843. [doi: 10.1109/CVPR.2017.618]
[28] Haris M, Shakhnarovich G, Ukita N. Deep back-projection network for super-resolution. In: Proc. of the IEEE Int’l Conf. on
Computer Vision and Pattern Recognition (CVPR). 2018. 1664−1673. [doi: 10.1109/CVPR.2018.00179]