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江泽涛  等:一种基于 MDARNet 的低照度图像增强方法                                                   3991


         [21]    Wang F, David MJT. Survey on the Attention based RNN model and its applications in computer vision. arXiv preprint arXiv:1601.
             06823, 2016.
         [22]    Ashish V, Noam S, Niki P, et al. Attention is All You Need. NIPS 2017: In: Proc. of the 31st Int’l Conf. on Neural Information
             Processing Systems, 2017. 6000−6010.
         [23]    Zhang H, Goodfellow I, Metaxas D, et al. Self-attention generative adversarial networks. arXiv preprint arXiv:1805.08318, 2018.
         [24]    Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition
             (CVPR). 2018. 7132−7141.
         [25]    Chen D, He ZW, Cao  YP,  et  al.  Deep neural network for fast  and  accurate single image super-resolution via  channel-
             attention-based fusion of orientation-aware features. arXiv preprint arXiv: 1912.04016, 2019.
         [26]    Wang Y, Cao Y, Zha ZJ, et al. Progressive Retinex: Mutually reinforced illumination-noise perception network for low light image
             enhancement. In: Proc. of the 27th ACM Int’l Conf. on Multimedia (MM 2019). 2019. 1−9. [doi: 10.1145/3343031.3350983]
         [27]    Lv FF, Lu F. Attention-guided low-light image enhancement. arXiv preprint arXiv: 1908.00682, 2019. 1−12.
         [28]    Qin  LL.  Research on low-light image  enhancement based on generative  adversarial networks  [MS.  Thesis].  Guilin: Guilin
             University of Electronic Technology, 2020 (in Chinese with English abstact).
         [29]    Gao H,  Zhuang L, Laurens VDM,  et al.  Densely  connected  convolutional networks.  In: Proc. of the IEEE  Conf. on  Computer
             Vision and Pattern Recognition (CVPR). 2017. 4700−4708. [doi: 10.1109/CVPR.2017.243]
         [30]    Ding  XH, Guo  YC, Ding GG,  et al.  ACNet: Strengthening the kernel skeletons for powerful  CNN via  asymmetric  convolution
             blocks. arXiv preprint arXiv: 1908.03930, 2019.
         [31]    Lee C, Lee C, Kim CS. Contrast enhancement based on layered difference representation. In: Proc. of the 19th IEEE Int’l Conf. on
             Image Processing (ICIP). Orlando, 2013. 965−968. [doi: 10.1109/ICIP.2012.6467022]
         [32]    Chen ZY, Jiang TT, Tian YH. Quality assessment for comparing image enhancement algorithms. In: Proc. of the IEEE Conf. on
             Computer Vision and Pattern Recognition (CVPR). Ohio, 2014. 3003−3010. [doi: 10.1109/CVPR.2014.384]
         [33]    Vonikakis  V, Kouskouridas R, Gasteratos A.  On  the evaluation  of illumination compensation algorithms. Multimedia  Tools &
             Applications, 2018,77:9211−9231.
         [34]    Brown M, Susstrunk S. Multi-spectral SIFT for scene category recognition. In: Proc. of the IEEE Conf. on Computer Vision and
             Pattern Recognition (CVPR). 2011. 177−184. [doi: 10.1109/CVPR.2011.5995637]
         [35]    Loh YP, Chan CS. Getting to know low-light images with the exclusively dark dataset. Computer Vision and Image Understanding,
             2019,178:30−42. [doi: 10.1016/j.cviu.2018.10.010]

         附中文参考文献:
         [18]  江泽涛,覃露露.一种基于 U-Net 生成对抗网络的低照度图像增强方法.电子学报,2020,48(2):258−264.
         [19]  江泽涛,伍旭,张少钦.一种基于 MR-VAE 的低照度图像增强方法.计算机学报,2019,12:1−13. http://kns.cnki.net/kcms/detail/11.
             1826.TP.20191202.1403.002.html
         [28]   覃露露.基于生成对抗网络的低照度图像增强方法研究[硕士学位论文].桂林:桂林电子科技大学,2020


                       江泽涛(1961-),男,博士,教授,博士生导                      秦嘉奇(1993-),男,硕士,主要研究领域为
                       师 , 主要 研究 领域 为 深 度 学 习 , 计算 机                计算机视觉.
                       视觉.



                       覃露露(1993-),女,硕士,主要研究领域为                      张少钦(1962-),女,教授,主要研究领域为
                       深度学习,计算机视觉.                                  图像处理,力学行为关系.
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