• Laser & Optoelectronics Progress
  • Vol. 55, Issue 9, 91003 (2018)
Cheng Deqiang, Cai Yingchun, Chen Liangliang, and Song Yulong
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  • [in Chinese]
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    DOI: 10.3788/lop55.091003 Cite this Article Set citation alerts
    Cheng Deqiang, Cai Yingchun, Chen Liangliang, Song Yulong. Multi-Scale Convolutional Neural Network Reconstruction Algorithm Based on Edge Correction[J]. Laser & Optoelectronics Progress, 2018, 55(9): 91003 Copy Citation Text show less
    References

    [1] Xiao J S, Liu E Y, Zhu L, et al. Improved image super-resolution algorithm based on convolutional neural network[J]. Acta Optica Sinica, 2017, 37(3): 0318011.

    [2] Btz M, Eichenseer A, Seiler J, et al. Hybrid super-resolution combining example-based single-image and interpolation-based multi-image reconstruction approaches[C]∥Proceedings of IEEE International Conference on Image Processing, 2015: 58-62.

    [3] Zhou J H, Zhou C, Zhu J J, 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.

    [4] Lian Q S, Zhang W. Image super-resolution algorithms based on sparse representation of classified image patches[J]. Acta Electronica Sinica, 2012, 40(5): 920-925.

    [5] Yang J C, Wright J, Huang T, et al. Image super-resolution as sparse representation of raw image patches[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2008: 1-8.

    [6] 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.

    [7] Timofte R, De V, van Gool L. Anchored neighborhood regression for fast example-based super-resolution[C]∥Proceedings of IEEE International Conference on Computer Vision, 2013: 1920-1927.

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

    [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] Dong C, Chen C L, Tang X. Accelerating the super-resolution convolutional neural network[C]∥Proceedings of European Conference on Computer Vision, 2016: 391-407.

    [11] Kim J, Lee J K, Lee K M. Deeply-recursive convolutional network for image super-resolution[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016: 1637-1645.

    [12] Sun C, Lü J W, Li J W, et al. Method of rapid image super-resolution based on deconvolution[J]. Acta Optica Sinica, 2017, 37(12): 1210004.

    [13] Chang H, Yeung D Y, Xiong Y M. Super-resolution through neighbor embedding[C]∥Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, 1: I.

    [14] Aharon M, Elad M, Bruckstein A. rmK-SVD: An algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322.

    [15] Jia Y Q, Shelhamer E, Donahue J, et al. Caffe: Convolutional architecture for fast feature embedding[C]∥Proceedings of the 22nd ACM International Conference on Multimedia, 2014: 675-678.

    [16] Bevilacqua M, Roumy A, Guillemot C, et al. Low-complexity single image super-resolution based on nonnegative neighbor embedding[EB/OL]. [2018-02-05]http:∥eprints.imtlucca.it/2412/1/Bevilacqua_2012.pdf.

    [17] Zeyde R, Elad M, Protter M. On single image scale-up using sparse-representations[C]∥Proceedings of International Conference on Curves and Surfaces, 2010: 711-730.

    Cheng Deqiang, Cai Yingchun, Chen Liangliang, Song Yulong. Multi-Scale Convolutional Neural Network Reconstruction Algorithm Based on Edge Correction[J]. Laser & Optoelectronics Progress, 2018, 55(9): 91003
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