• Opto-Electronic Engineering
  • Vol. 48, Issue 6, 210009 (2021)
Ji Xunsheng and Teng Bin*
Author Affiliations
  • [in Chinese]
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    DOI: 10.12086/oee.2021.210009 Cite this Article
    Ji Xunsheng, Teng Bin. Deblurring algorithm based on pedestrian abnormal behavior generation countermeasure network[J]. Opto-Electronic Engineering, 2021, 48(6): 210009 Copy Citation Text show less
    References

    [2] Sun J, Cao W F, Xu Z B, et al. Learning a convolutional neural network for non-uniform motion blur removal[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, 2015: 769–777.

    [3] Nimisha T M, Singh A K, Rajagopalan A N. Blur-invariant deep learning for blind-deblurring[C]//Proceedings of the IEEE International Conference on Computer Vision, Venice, 2017: 4762–4770.

    [4] Ramakrishnan S, Pachori S, Gangopadhyay A, et al. Deep generative filter for motion deblurring[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, 2017: 2993–3000.

    [5] Shao W Z, Liu Y Y, Ye L Y, et al. DeblurGAN+: Revisiting blind motion deblurring using conditional adversarial networks[J]. Signal Processing, 2020, 168: 107338.

    [6] Wang X T, Ke Y, Wu S X, et al. ESRGAN: enhanced super-resolution generative adversarial networks[C]//European Conference on Computer Vision, Munich, 2018: 6379.

    [8] Yu J, Chang Z C, Xiao C B, et al. Blind image deblurring based on sparse representation and structural self-similarity[C]//2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, 2017: 1328–1332.

    [11] Li C, Wand M. Precomputed real-time texture synthesis with markovian generative adversarial networks[C]//Proceedings of European Conference on Computer Vision, Amsterdam, 2016: 702–716.

    [12] Gulrajani I, Ahmend F, Arjovsky M, et al. Improved training of Wassertstein GANs[EB/OL]. (2017-12-25)[2018-08-15]. https://arxiv.org/pdf/1704.00028.pdf.

    [13] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2015-04-10)[2018-08-17]. https://arxiv.org/pdf/1409.1556.pdf.

    [14] Nah S, Kim T H, Lee K M. Deep multiscale convolutional neural network for dynamic scene deblurring[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 2017: 257–265.

    [15] Redmon J, Farhadi A. YOLOv3: An Incremental Improvement[Z]. arXiv: 1804.02767, 2018.

    [16] Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. J Mach Learn Res, 2014, 15(1): 1929–1958.

    [19] 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, 2018: 2472–2481.

    [21] Zhou L H, Min W D, Lin D Y, et al. Detecting motion blurred vehicle logo in IoV Using Filter-DeblurGAN and VL-YOLO[J]. IEEE Trans Veh Technol, 2020, 69(4): 3604–3614.

    [22] Truong N Q, Lee Y W, Owais M, et al. SlimDeblurGAN-based motion deblurring and marker detection for autonomous drone landing[J]. Sensors, 2020, 20(14): 3918.

    [23] Kupyn O, Martyniuk T, Wu J R, et al. DeblurGAN-v2: deblurring (Orders-of-Magnitude) faster and better[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, 2019: 8877–8886.

    Ji Xunsheng, Teng Bin. Deblurring algorithm based on pedestrian abnormal behavior generation countermeasure network[J]. Opto-Electronic Engineering, 2021, 48(6): 210009
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