[1]王凡超,丁世飞.基于广泛激活深度残差网络的图像超分辨率重建[J].智能系统学报,2022,17(2):440-446.[doi:10.11992/tis.202106023]
 WANG Fanchao,DING Shifei.Image super-resolution reconstruction based on widely activated deep residual networks[J].CAAI Transactions on Intelligent Systems,2022,17(2):440-446.[doi:10.11992/tis.202106023]
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基于广泛激活深度残差网络的图像超分辨率重建

参考文献/References:
[1] 雷鹏程, 刘丛, 唐坚刚, 等. 分层特征融合注意力网络图像超分辨率重建[J]. 中国图象图形学报, 2020, 25(9): 1773–1786
LEI Pengcheng, LIU Cong, TANG Jiangang, et al. Hierarchical feature fusion attention network for image super-resolution reconstruction[J]. Journal of image and graphics, 2020, 25(9): 1773–1786
[2] HUANG Zhengzhong, CAO Liangcai. Bicubic interpolation and extrapolation iteration method for high resolution digital holographic reconstruction[J]. Optics and lasers in engineering, 2020, 130: 106090.
[3] HAO Sai, DONG Xianghuai. Interpolation-based plane stress anisotropic yield models[J]. International journal of mechanical sciences, 2020, 178: 105612.
[4] 贺璟, 郝晓丽, 吕进来. 梯度插值与可变阈值改进的POCS算法[J]. 中国科技论文, 2017, 12(14): 1655–1658,1684
HE Jing, HAO Xiaoli, Lü Jinlai. POCS algorithm based on gradient interpolation and variable threshold[J]. China sciencepaper, 2017, 12(14): 1655–1658,1684
[5] 白蔚, 杨撒博雅, 刘家瑛, 等. 基于显著性稀疏表示的图像超分辨率算法[J]. 中国科技论文, 2014, 9(1): 103–107
BAI Wei, YANG Saboya, LIU Jiaying, et al. Image super resolution based on salient sparse coding[J]. China sciencepaper, 2014, 9(1): 103–107
[6] DONG Chao, LOY C C, HE Kaiming, et al. Learning a deep convolutional network for image super-resolution[C]//Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerland, 2014: 184?199.
[7] KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016: 1646?1654.
[8] LIM B, SON S, KIM H, et al. Enhanced deep residual networks for single image super-resolution[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu, USA, 2017: 136?144.
[9] IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 32nd International Conference on Machine Learning. Lille, France, 2015: 448-456.
[10] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2015-04-10)[2021-01-01].https://arxiv.org/abs/1409.1556.
[11] SHI Wenzhe, CABALLERO J, HUSZáR F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016: 1874-1883.
[12] LEDIG C, THEIS L, HUSZáR F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017: 4681-4690.
[13] ZHANG Yulun, TIAN Yapeng, KONG Yu, et al. Residual dense network for image super-resolution[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018: 2472-2481.
[14] YU Jiahui, FAN Yuchen, YANG Jianchao, et al. Wide activation for efficient and accurate image super-resolution[EB/OL]. (2018-12-21)[2021-01-01]. https://arxiv.org/abs/1808.08718v1.
[15] WANG Xintao, YU Ke, WU Shixiang, et al. ESRGAN: enhanced super-resolution generative adversarial networks[C]//Proceedings of the European Conference on Computer Vision. Munich, Germany, 2018: 63?79.
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备注/Memo

收稿日期:2021-06-15。
基金项目:国家自然科学基金项目(61976216,61672522)
作者简介:王凡超,硕士研究生,主要研究方向为深度学习、图像超分辨率;丁世飞,教授,博士生导师,CCF杰出会员,第八届吴文俊人工智能科学技术奖获得者,主要研究方向为人工智能与模式识别、机器学习与数据挖掘。主持国家重点基础研究计划课题1项、国家自然科学基金面上项目3项。出版专著5部,发表学术论文200余篇
通讯作者:丁世飞.E-mail:dingsf@cumt.edu.cn

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