• Laser & Optoelectronics Progress
  • Vol. 58, Issue 8, 0810005 (2021)
Tibo Zha, Lin Luo, Kai Yang*, Yu Zhang, and Jinlong Li
Author Affiliations
  • School of Physical Science and Technology, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
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    DOI: 10.3788/LOP202158.0810005 Cite this Article Set citation alerts
    Tibo Zha, Lin Luo, Kai Yang, Yu Zhang, Jinlong Li. Image Reconstruction Algorithm Based on Improved Super-Resolution Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2021, 58(8): 0810005 Copy Citation Text show less
    References

    [1] Xie C, Zeng W L, Lu X B. Fast single-image super-resolution via deep network with component learning[J]. IEEE Transactions on Circuits and Systems for Video Technology, 29, 3473-3486(2019). http://ieeexplore.ieee.org/document/8550684/

    [2] 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, 38, 295-307(2016). http://www.ncbi.nlm.nih.gov/pubmed/26761735

    [3] Dong C, Loy C C, Tang X. Accelerating the super-resolution convolutional neural network[M]. //Leibe B, Matas J, Sebe N, et al. Computer Vision-ECCV 2016. Lecture Notes in Computer Science. Cham: Springer, 9906, 391-407(2016).

    [4] Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 1646-1654(2016).

    [5] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

    [6] Kim J, Lee J K, Lee K M. Deeply-recursive convolutional network for image super-resolution[EB/OL]. [2020-07-04]. https://arxiv.org/abs/1511.04491

    [7] Shi W Z, Caballero J, Huszár F et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 1874-1883(2016).

    [8] Caballero J, Ledig C, Aitken A et al. Real-time video super-resolution with spatio-temporal networks and motion compensation[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2848-2857(2017).

    [9] Hu S Y, Wang G D, Zhao Y et al. Image super-resolution network based on dense connection and squeeze module[J]. Laser & Optoelectronics Progress, 56, 201005(2019).

    [10] Yang C Y, Ma C, Yang M H. Single-image super-resolution: a benchmark[M]. //Fleet D, Pajdla T, Schiele B, et al. Computer Vision-ECCV 2014. Lecture Notes in Computer Science. Cham: Springer, 8692, 372-386(2014).

    [11] Zhang S F, Zhang C, Zhang T et al. Review on universal no-reference image quality assessment algorithm[J]. Computer Engineering and Applications, 51, 13-23, 151(2015).

    [12] Goodfellow I J, Pouget A J, Mirza M et al. Generative adversarial nets[EB/OL]. [2020-07-02]. https:∥arxiv.org/abs/1511.04491

    [13] Ledig C, Theis L, Huszár F et al. Photo-realistic single image super-resolution using a generative adversarial network[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 105-114(2017).

    [14] Johnson J, Alahi A, Li F F. Perceptual losses for real-time style transfer and super-resolution[M]. //Leibe B, Matas J, Sebe N, et al. Computer Vision-ECCV 2016. Lecture Notes in Computer Science. Cham: Springer, 9906, 694-711(2016).

    [15] Lim B, Son S, Kim H et al. Enhanced deep residual networks for single image super-resolution[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 21-26, 2017, Honolulu, HI, USA., 1132-1140(2017).

    [16] Jolicoeur-Martineau A. The relativistic discriminator: a key element missing from standard GAN[EB/OL]. [2020-07-02]. https://arxiv.org/abs/1807.00734

    [17] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[EB/OL]. [2020-07-04]. https://arxiv.org/abs/1502.03167

    [18] Wang X T, Yu K, Wu S X et al. ESRGAN: enhanced super-resolution generative adversarial networks[M]. //Leal-Taixé L, Roth S, et al. Computer Vision-ECCV 2018. Lecture Notes in Computer Science. Cham: Springer, 11133, 63-79(2019).

    [19] Gulrajani I, Ahmed F, Arjovsky M et al. Improved training of Wasserstein GANs[EB/OL]. [2020-07-05]. http://arxiv.org/abs/1704.00028

    [20] Lin T Y, Maire M, Belongie S et al. Microsoft COCO: common objects in context[M]. //Fleet D, Pajdla T, Schiele B, et al. Computer Vision-ECCV 2014. Lecture Notes in Computer Science. Cham: Springer, 8693, 740-755(2014).

    [21] Yu W, Xu J J, Liu Y Y et al. No-reference quality evaluation for gamut mapping images based on natural scene statistics[J]. Laser & Optoelectronics Progress, 57, 141006(2020).

    Tibo Zha, Lin Luo, Kai Yang, Yu Zhang, Jinlong Li. Image Reconstruction Algorithm Based on Improved Super-Resolution Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2021, 58(8): 0810005
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