<-上一篇/Previous Article 下一篇/Next Article->

[1]田春伟,宋明键,左旺孟,等.卷积神经网络在图像超分辨上的应用[J].智能系统学报,2025,20(3):719-749.[doi:10.11992/tis.202409027]
　TIAN Chunwei,SONG Mingjian,ZUO Wangmeng,et al.Application of convolutional neural networks in image super-resolution[J].CAAI Transactions on Intelligent Systems,2025,20(3):719-749.[doi:10.11992/tis.202409027]

点击复制

卷积神经网络在图像超分辨上的应用

PDF下载 HTML

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 20 期数: 2025年第3期页码: 719-749 栏目: 人工智能校长论坛出版日期: 2025-05-05

Title:: Application of convolutional neural networks in image super-resolution

作者:: 田春伟^1,2, 宋明键³, 左旺孟¹, 杜博⁴, 张艳宁^2,5, 张师超⁶; 1. 哈尔滨工业大学计算学部, 黑龙江哈尔滨 150001;
2. 空天地海一体化大数据应用技术国家工程实验室, 陕西西安 710192;
3. 西北工业大学软件学院, 陕西西安 710072;
4. 武汉大学计算机学院, 湖北武汉 430072;
5. 西北工业大学计算机学院, 陕西西安 710072?;
6. 广西师范大学计算机科学与工程学院, 广西桂林 541004

Author(s):: TIAN Chunwei^1,2, SONG Mingjian³, ZUO Wangmeng¹, DU Bo⁴, ZHANG Yanning^2,5, ZHANG Shichao⁶; 1. Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China;
2. National Engineering Laboratory for Big Data Application Technology of Integrated Space, Air, Ground, and Sea, Xi’an 710192, China;
3. School of Software, Northwestern Polytechnical University, Xi’an 710072, China;
4. School of Computer Science, Wuhan University, Wuhan 430072, China;
5. School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China;
6. School of Computer Science and Engineering, Guangxi Normal University, Guilin 541004, China

关键词:: 深度学习; 卷积神经网络; 图像重建; 图像处理; 图像复原; 图像分辨率; 神经网络; 底层视觉

Keywords:: deep learning; convolutional neural networks; image reconstruction; image processing; image restoration; image resolution; neural networks; low-level vision

分类号:: TP391.41

DOI:: 10.11992/tis.202409027

摘要:: 卷积神经网络因强大的学习能力，已成为解决图像超分辨问题的主流方法。然而，用于解决图像超分辨的不同类型深度学习方法存在巨大的差异。目前，仅有少量文献能根据不同缩放方法来总结不同深度学习技术在图像超分辨上的区别和联系。因此，根据设备的负载能力和执行速度等介绍面向图像超分辨方法的卷积神经网络尤为重要。本文首先介绍面向图像超分辨的卷积神经网络基础，随后通过介绍基于双三次插值、最近邻插值、双线性插值、转置卷积、亚像素层、元上采样的卷积神经网络的图像超分辨方法，分析基于插值和模块化的卷积神经网络图像超分辨方法的区别与联系，并通过实验比较这些方法的性能。本文对潜在的研究方向和挑战进行阐述并总结全文，旨在促进基于卷积神经网络的图像超分辨研究的发展。

Abstract:: Known for their strong learning abilities, convolutional neural networks (CNNs) have become mainstream methods for image super-resolution. However, substantial differences exist among deep learning methods of various types, and there is limited literature to summarize the relations and differences of different methods in image super-resolution. Thus, it is important to summarize such studies according to the loading capacity and the execution speed of devices. This paper first introduces the principles of CNNs in image super-resolution and then introduces CNN-based bicubic interpolation, nearest neighbor interpolation, bilinear interpolation, transposed convolution, subpixel layering, and meta-upsampling for image super-resolution to analyze the differences and relations of different CNN-based interpolations and modules. The performance of these methods is compared through experiments. Finally, this paper presents potential research points and drawbacks and summarizes the whole paper to promote the development of CNNs in image super-resolution.

参考文献/References:: [1] 史振威, 雷森. 图像超分辨重建算法综述[J]. 数据采集与处理, 2020, 35(1): 1-20.
SHI Zhenwei, LEI Sen. Review of image super-resolution reconstruction[J]. Journal of data acquisition and processing, 2020, 35(1): 1-20.
[2] ZHANG Liangpei, ZHANG Hongyan, SHEN Huanfeng, et al. A super-resolution reconstruction algorithm for surveillance images[J]. Signal processing, 2010, 90(3): 848-859.
[3] CHAN K C K, ZHOU Shangchen, XU Xiangyu, et al. BasicVSR: improving video super-resolution with enhanced propagation and alignment[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans: IEEE, 2022: 5962-5971.
[4] RUKUNDO O, CAO Hanqiang. Nearest neighbor value interpolation[EB/OL]. (2012-11-08)[2024-01-01]. https://arxiv.org/abs/1211.1768v2.
[5] ELAD M, FEUER A. Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images[J]. IEEE transactions on image processing, 1997, 6(12): 1646-1658.
[6] PRAJAPATI A, NAIK S, MEHTA S. Evaluation of different image interpolation algorithms[J]. International journal of computer applications, 2012, 58(12): 6-12.
[7] YANG Jianchao, WRIGHT J, HUANG T S, et al. Image super-resolution via sparse representation[J]. IEEE transactions on image processing, 2010, 19(11): 2861-2873.
[8] CHEESEMAN P, KANEFSKY B, KRAFT R, et al. Super-resolved surface reconstruction from multiple images[M]//Maximum Entropy and Bayesian Methods. Dordrecht: Springer Netherlands, 1996: 293-308.
[9] IRANI M, PELEG S. Super resolution from image sequences[C]// Proceedings 10th International Conference on Pattern Recognition. Atlantic City: IEEE, 1990: 115-120.
[10] KOMATSU T, IGARASHI T, AIZAWA K, et al. Very high resolution imaging scheme with multiple different-aperture cameras[J]. Signal processing: image communication, 1993, 5(5/6): 511-526.
[11] AGUENA M, MASCARENHAS N. Generalization of iterative restoration techniques for super-resolution[C]//2011 24th SIBGRAPI Conference on Graphics, Patterns and Images. Alagoas: IEEE, 2011: 258-265.
[12] GAO Xinbo, ZHANG Kaibing, TAO Dacheng, et al. Image super-resolution with sparse neighbor embedding[J]. IEEE transactions on image processing, 2012, 21(7): 3194-3205.
[13] VAN OUWERKERK J D. Image super-resolution survey[J]. Image and vision computing, 2006, 24(10): 1039-1052.
[14] YU Hong, XIANG Ma, HUA Huang, et al. Face image super-resolution through POCS and residue compensation[C]//2008 5th International Conference on Visual Information Engineering (VIE 2008). Xi’an: IET, 2008: 494-497.
[15] TIAN Chunwei, ZHANG Xuanyu, ZHU Qi, et al. Generative adversarial networks for image super-resolution: a survey[EB/OL]. (2022-04-28)[2024-01-01]. https://arxiv.org/abs/2204.13620v4.
[16] DONG Chao, LOY C C, HE Kaiming, et al. Image super-resolution using deep convolutional networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2016, 38(2): 295-307.
[17] 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. Las Vegas: IEEE, 2016: 1646-1654.
[18] DONG Chao, LOY C C, TANG Xiaoou. Accelerating the super-resolution convolutional neural network[C]//Computer Vision–ECCV 2016. Amsterdam: Springer International Publishing, 2016: 391-407.
[19] CHOI J S, KIM M. A deep convolutional neural network with selection units for super-resolution[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu: IEEE, 2017: 1150-1156.
[20] KIM J, LEE J K, LEE K M. Deeply-recursive convolutional network for image super-resolution[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 1637-1645.
[21] TONG Tong, LI Gen, LIU Xiejie, et al. Image super-resolution using dense skip connections[C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 4809-4817.
[22] ZHANG Yulun, TIAN Yapeng, KONG Yu, et al. Residual dense network for image super-resolution[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 2472-2481.
[23] ZHANG Yulun, LI Kunpeng, LI Kai, et al. Image super-resolution using very deep residual channel attention networks[C]//Computer Vision–ECCV 2018. Cham: Springer International Publishing, 2018: 294-310.
[24] DAI Tao, CAI Jianrui, ZHANG Yongbing, et al. Second-order attention network for single image super-resolution[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 11057-11066.
[25] 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. Honolulu: IEEE, 2017: 105-114.
[26] WANG Xintao, YU Ke, WU Shixiang, et al. ESRGAN: enhanced super-resolution generative adversarial networks[C]//Computer Vision– ECCV 2018 Workshops. Cham: Springer International Publishing, 2019: 63-79.
[27] 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.
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.
[28] 刘玉铠, 周登文. 基于多路特征渐进融合和注意力机制的轻量级图像超分辨率重建[J]. 智能系统学报, 2024, 19(4): 863-873.
LIU Yukai, ZHOU Dengwen. Lightweight super-resolution reconstruction via progressive multi-path feature fusion and attention mechanism[J]. CAAI transactions on intelligent systems, 2024, 19(4): 863-873.
[29] YANG Wenming, ZHANG Xuechen, TIAN Yapeng, et al. Deep learning for single image super-resolution: a brief review[J]. IEEE transactions on multimedia, 2019, 21(12): 3106-3121.
[30] SUN Jian, SUN Jian, XU Zongben, et al. Gradient profile prior and its applications in image super-resolution and enhancement[J]. IEEE transactions on image processing, 2011, 20(6): 1529-1542.
[31] WANG Zhihao, CHEN Jian, HOI S C H. Deep learning for image super-resolution: a survey[J]. IEEE transactions on pattern analysis and machine intelligence, 2021, 43(10): 3365-3387.
[32] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2014-12-23)[2024-01-01]. https://arxiv.org/abs/1409.1556v6.
[33] CHEN Jin, ZHU Xiaolin, VOGELMANN J E, et al. A simple and effective method for filling gaps in Landsat ETM+ SLC-off images[J]. Remote sensing of environment, 2011, 115(4): 1053-1064.
[34] PARK S C, PARK M K, KANG M G. Super-resolution image reconstruction: a technical overview[J]. IEEE signal processing magazine, 2003, 20(3): 21-36.
[35] KEYS R. Cubic convolution interpolation for digital image processing[J]. IEEE transactions on acoustics, speech, and signal processing, 1981, 29(6): 1153-1160.
[36] KIRKLAND E J. Advanced computing in electron microscopy[M]. Boston: Springer US, 1998.
[37] 周登文, 赵丽娟, 段然, 等. 基于递归残差网络的图像超分辨率重建[J]. 自动化学报, 2019, 45(6): 1157-1165.
ZHOU Dengwen, ZHAO Lijuan, DUAN Ran, et al. Image super-resolution based on recursive residual networks[J]. ACTA AUTOMATICA SINICA, 2019, 45(6): 1157-1165.
[38] REN Haoyu, EL-KHAMY M, LEE J. Image super resolution based on fusing multiple convolution neural networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu: IEEE, 2017: 1050-1057.
[39] TAI Ying, YANG Jian, LIU Xiaoming, et al. MemNet: a persistent memory network for image restoration[C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 4549-4557.
[40] HU Yanting, GAO Xinbo, LI Jie, et al. Single image super-resolution via cascaded multi-scale cross network[EB/OL]. (2018-02-24)[2024-01-01]. https://arxiv.org/abs/1802.08808v1.
[41] YAMANAKA J, KUWASHIMA S, KURITA T. Fast and accurate image super resolution by deep CNN with skip connection and network in network[EB/OL].(2017-07-18)[2024-01-01]. https://arxiv.org/abs/1707.05425.
[42] 陈子涵, 吴浩博, 裴浩东, 等. 基于自注意力深度网络的图像超分辨率重建方法[J]. 激光与光电子学进展, 2021, 58(4): 0410013.
CHEN Zihan, WU Haobo, PEI Haodong, et al. Image super-resolution reconstruction method based on self-attention deep network[J]. Laser & optoelectronics progress, 2021, 58(4): 0410013.
[43] TAI Ying, YANG Jian, LIU Xiaoming. Image super-resolution via deep recursive residual network[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 2790-2798.
[44] 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. Honolulu: IEEE, 2017: 1132-1140.
[45] WANG Xintao, XIE Liangbin, DONG Chao, et al. Real-ESRGAN: training real-world blind super-resolution with pure synthetic data[C]//2021 IEEE/CVF International Conference on Computer Vision Workshops. Montreal: IEEE, 2021: 1905-1914.
[46] TIAN Chunwei, XU Yong, ZUO Wangmeng, et al. Coarse-to-fine CNN for image super-resolution[J]. IEEE transactions on multimedia, 2020, 23: 1489-1502.
[47] RAD M S, YU T, MUSAT C, et al. Benefiting from bicubically down-sampled images for learning real-world image super-resolution[C]//2021 IEEE Winter Conference on Applications of Computer Vision. Waikoloa: IEEE, 2021: 1590-1599.
[48] DEMIRAY B Z, SIT M, DEMIR I. D-SRGAN: DEM super-resolutionwith generative adversarial networks[J]. SN computer science, 2021, 2(1): 48.
[49] ZHANG Lina, DAI Haidong, SANG Yu. Med-SRNet: GAN-based medical image super-resolution via high-resolution representation learning[J]. Computational intelligence and neuroscience, 2022, 2022(1): 1744969.
[50] WANG Yifan, PERAZZI F, MCWILLIAMS B, et al. A fully progressive approach to single-image super-resolution[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City: IEEE, 2018: 97701-97709.
[51] NI Min, LEI Jianjun, CONG Runmin, et al. Color-guided depth map super resolution using convolutional neural network[J]. IEEE access, 2017, 5: 26666-26672.
[52] HAN Xianhua, SHI Boxin, ZHENG Yinqiang. SSF-CNN: spatial and spectral fusion with CNN for hyperspectral image super-resolution[C]//2018 25th IEEE International Conference on Image Processing. Athens: IEEE, 2018: 2506-2510.
[53] WANG Zhaowen, LIU Ding, YANG Jianchao, et al. Deep networks for image super-resolution with sparse prior[C]//2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 370-378.
[54] HUI Zheng, WANG Xiumei, GAO Xinbo. Fast and accurate single image super-resolution via information distillation network[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 723-731.
[55] YUAN Yuan, LIU Siyuan, ZHANG Jiawei, et al. Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City: IEEE, 2018: 81401-81409.
[56] MAEDA S. Unpaired image super-resolution using pseudo-supervision[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 291-300.
[57] ZHANG Kai, ZUO Wangmeng, ZHANG Lei. Deep plug-and-play super-resolution for arbitrary blur kernels[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 1671-1681.
[58] GU Jinjin, LU Hannan, ZUO Wangmeng, et al. Blind super-resolution with iterative kernel correction[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 1604-1613.
[59] ZHOU Ruofan, SUSSTRUNK S. Kernel modeling super-resolution on real low-resolution images[C]//2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 2433-2443.
[60] KLIGLER S B, SHOCHER A, IRANI M. Blind super-resolution kernel estimation using an internal-GAN[EB/OL]. (2019-09-14)[2024-01-01]. https://arxiv.arg.org/abs/1909.06581.
[61] WANG Longguang, WANG Yingqian, DONG Xiaoyu, et al. Unsupervised degradation representation learning for blind super-resolution[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 10581-10590.
[62] ZHANG Kai, LIANG Jingyun, VAN GOOL L, et al. Designing a practical degradation model for deep blind image super-resolution[C]//2021 IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 4771-4780.
[63] ZHANG Jiahui, LU Shijian, ZHAN Fangneng, et al. Blind image super-resolution via contrastive representation learning[EB/OL]. (2021-07-01)[2024-01-01]. https://arxiv.org/abs/2107.00708v1.
[64] WU Yixuan, LI Feng, BAI Huihui, et al. Bridging component learning with degradation modelling for blind image super-resolution[J]. IEEE transactions on multimedia, 2022(99): 1-16.
[65] CAO Xiang, LUO Yihao, XIAO Yi, et al. Blind image super-resolution based on prior correction network[J]. Neurocomputing, 2021, 463: 525-534.
[66] YAMAWAKI K, SUN Yongqing, HAN Xianhua. Blind image super resolution using deep unsupervised learning[J]. Electronics, 2021, 10(21): 2591.
[67] YAMAWAKI K, HAN Xianhua. Deep blind un-supervised learning network for single image super resolution[C]//2021 IEEE International Conference on Image Processing. Anchorage: IEEE, 2021: 1789-1793.
[68] 丁宇胜. 数字图像处理中的插值算法研究[J]. 电脑知识与技术, 2010, 6(16): 4502-4503,4506.
DING Yusheng. Interpolated algorithm research in digital image processing[J]. Computer knowledge and technology, 2010, 6(16): 4502-4503,4506.
[69] ZHANG Kai, VAN GOOL L, TIMOFTE R. Deep unfolding network for image super-resolution[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 3217-3226.
[70] FAN Yuchen, SHI Honghui, YU Jiahui, et al. Balanced two-stage residual networks for image super-resolution[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu: IEEE, 2017: 1157-1164.
[71] SAJJADI M S M, SCH?LKOPF B, HIRSCH M. EnhanceNet: single image super-resolution through automated texture synthesis[C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 4501-4510.
[72] SHANG Taizhang, DAI Qiuju, ZHU Shengchen, et al. Perceptual extreme super resolution network with receptive field block[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle: IEEE, 2020: 440-441.
[73] ZHAO Hengyuan, KONG Xiangtao, HE Jingwen, et al. Efficient image super-resolution using pixel attention[EB/OL]. (2020-10-02)[2024-01-01]. https://arxiv.arg.org/abs/2010.01073.
[74] HUANG Zilong, WANG Xinggang, HUANG Lichao, et al. CCNet: criss-cross attention for semantic segmentation[C]//2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 603-612.
[75] JO Y, KIM S J. Practical single-image super-resolution using look-up table[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 691-700.
[76] LIANG Jingyun, SUN Guolei, ZHANG Kai, et al. Mutual affine network for spatially variant kernel estimation in blind image super-resolution[C]//2021 IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 4076-4085.
[77] HE Zongyao, JIN Zhi, ZHAO Yao. SRDRL: a blind super-resolution framework with degradation reconstruction loss[J]. IEEE transactions on multimedia, 2021, 24: 2877-2889.
[78] YOUM G Y, BAE S H, KIM M. Image super-resolution based on convolution neural networks using multi-channel input[C]//2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop. Bordeaux: IEEE, 2016: 1-5.
[79] INTANIYOM T, THANANPORN W, WORARATPANYA K. Enhancement of anime imaging enlargement using modified super-resolution CNN[C]//2021 13th International Conference on Information Technology and Electrical Engineering. Chiang Mai: IEEE, 2021: 226-231.
[80] VU T, NGUYEN C V, PHAM T X, et al. Fast and efficient image quality enhancement via desubpixel convolutional neural networks[C]//ECCV 2018. Munich: Springer International Publishing, 2019: 243-259.
[81] LIU Hong, LU Zhisheng, SHI Wei, et al. A fast and accurate super-resolution network using progressive residual learning[C]//2020 IEEE International Conference on Acoustics, Speech and Signal Processing. Barcelona: IEEE, 2020: 1818-1822.
[82] ZHANG Yan, SUN Yemei, LIU Shudong. Deformable and residual convolutional network for image super-resolution[J]. Applied intelligence, 2022, 52(1): 295-304.
[83] KIM G, PARK J, LEE K, et al. Unsupervised real-world super resolution with cycle generative adversarial network and domain discriminator[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle: IEEE, 2020: 1862-1871.
[84] FAN Yuchen, YU Jiahui, LIU Ding, et al. Scale-wise convolution for image restoration[EB/OL]. (2019-12-19)[2024-01-01]. https://arxiv.org/abs/1912.09028.
[85] SURYANARAYANA G, CHANDRAN K, KHALAF O I, et al. Accurate magnetic resonance image super-resolution using deep networks and Gaussian filtering in the stationary wavelet domain[J]. IEEE access, 2021, 9: 71406-71417.
[86] UMER R M, MUNIR A, MICHELONI C. A deep residual star generative adversarial network for multi-domain image super-resolution[C]//2021 6th International Conference on Smart and Sustainable Technologies. Bol and Split: IEEE, 2021: 1-5.
[87] ZHU Mingyan, DAI Tao, XIA Shutao, et al. EDKE: encoder-decoder based kernel estimation for blind image super-resolution[C]//2021 International Joint Conference on Neural Networks. Shenzhen: IEEE, 2021: 1-7.
[88] WEI Yunxuan, GU Shuhang, LI Yawei, et al. Unsupervised real-world image super resolution via domain-distance aware training[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 13385-13394.
[89] LI Yue, LIU Dong, LI Houqiang, et al. Convolutional neural network-based block up-sampling for intra frame coding[J]. IEEE transactions on circuits and systems for video technology, 2018, 28(9): 2316-2330.
[90] MAO X, SHEN C, YANG Y B. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections[J]. Advances in neural information processing systems, 2016, 29.
[91] LAI Weisheng, HUANG Jiabin, AHUJA N, et al. Fast and accurate image super-resolution with deep Laplacian pyramid networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2019, 41(11): 2599-2613.
[92] SHI Jun, LI Zheng, YING Shihui, et al. MR image super-resolution via wide residual networks with fixed skip connection[J]. IEEE journal of biomedical and health informatics, 2019, 23(3): 1129-1140.
[93] LI Zhen, YANG Jinglei, LIU Zheng, et al. Feedback network for image super-resolution[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 3862-3871.
[94] ZENG Kai, DING Shifei, JIA Weikuan. Single image super-resolution using a polymorphic parallel CNN[J]. Applied intelligence, 2019, 49(1): 292-300.
[95] HARIS M, SHAKHNAROVICH G, UKITA N. Deep back-projection networks for super-resolution[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 1664-1673.
[96] YANG Xin, MEI Haiyang, ZHANG Jiqing, et al. DRFN: deep recurrent fusion network for single-image super-resolution with large factors[J]. IEEE transactions on multimedia, 2019, 21(2): 328-337.
[97] ZHONG Zhisheng, SHEN Tiancheng, YANG Yibo, et al. Joint sub-bands learning with clique structures for wavelet domain super-resolution[EB/OL]. (2018-10-25)[2024-01-01]. https://arxiv.org/abs/1809.04508v3.
[98] WANG Yifan, WANG Lijun, WANG Hongyu, et al. End-to-end image super-resolution via deep and shallow convolutional networks[J]. IEEE access, 2019, 7: 31959-31970.
[99] LI Juncheng, FANG Faming, MEI Kangfu, et al. Multi-scale residual network for image super-resolution[C]// Computer Vision-ECCV 2018. Cham: Springer International Publishing, 2018: 527-542.
[100] TAN Congming, CHENG Shuli, WANG Liejun. Efficient image super-resolution via self-calibrated feature fuse[J]. Sensors, 2022, 22(1): 329.
[101] CHENG Jianxin, KUANG Qiuming, SHEN Chenkai, et al. ResLap: generating high-resolution climate prediction through image super-resolution[J]. IEEE access, 2020, 8: 39623-39634.
[102] JIA Sen, WANG Zhihao, LI Qingquan, et al. Multiattention generative adversarial network for remote sensing image super-resolution[J]. IEEE transactions on geoscience and remote sensing, 2022, 60: 5624715.
[103] CHEN Yuantao, XIA Runlong, YANG Kai, et al. MFFN: image super-resolution via multi-level features fusion network[J]. The visual computer, 2024, 40(2): 489-504.
[104] ZHANG Xiaomin. Uncertainty-driven mixture convolution and transformer network for remote sensing image super-resolution[J]. Scientific reports, 2024, 14: 9435.
[105] BAI Haomou, LIANG Xiao. A very lightweight image super-resolution network[J]. Scientific reports, 2024, 14(1): 13850.
[106] DARGAHI S, AGHAGOLZADEH A, EZOJI M. Single image super-resolution by cascading parallel-structure units through a deep-shallow CNN[J]. Optik, 2023, 286: 171001.
[107] XU Xiangyu, SUN Deqing, PAN Jinshan, et al. Learning to super-resolve blurry face and text images[C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 251-260.
[108] TAO G, JI X, WANG W, et al. Spectrum-to-kernel translation for accurate blind image super-resolution[J]. Advances in neural information processing systems, 2021, 34: 22643-22654.
[109] FANG Zhenxuan, DONG Weisheng, LI Xin, et al. Uncertainty learning in kernel estimation for multi-stage blind image super-resolution[C]//ECCV 2022. Tel Aviv: Springer Nature Switzerland, 2022: 144-161.
[110] DU Yanbin, JIA Ruisheng, CUI Zhe, et al. X-ray image super-resolution reconstruction based on a multiple distillation feedback network[J]. Applied intelligence, 2021, 51(7): 5081-5094.
[111] 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]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 1874-1883.
[112] SONG Zhaoyang, ZHAO Xiaoqiang, JIANG Hongmei. Gradual deep residual network for super-resolution[J]. Multimedia tools and applications, 2021, 80(7): 9765-9778.
[113] JIANG Pengfei, LIN Weiguo, SHANG Wenqian. Single image super-resolution model based on improved sub-pixel convolutional neural network[C]//2021 IEEE International Conference on Power Electronics, Computer Applications. Shenyang: IEEE, 2021: 132-136.ems, 2020, 205: 106235.

相似文献/References:: [1]张媛媛,霍静,杨婉琪,等.深度信念网络的二代身份证异构人脸核实算法[J].智能系统学报,2015,10(2):193.[doi:10.3969/j.issn.1673-4785.201405060]
　ZHANG Yuanyuan,HUO Jing,YANG Wanqi,et al.A deep belief network-based heterogeneous face verification method for the second-generation identity card[J].CAAI Transactions on Intelligent Systems,2015,10():193.[doi:10.3969/j.issn.1673-4785.201405060]
[2]丁科,谭营.GPU通用计算及其在计算智能领域的应用[J].智能系统学报,2015,10(1):1.[doi:10.3969/j.issn.1673-4785.201403072]
　DING Ke,TAN Ying.A review on general purpose computing on GPUs and its applications in computational intelligence[J].CAAI Transactions on Intelligent Systems,2015,10():1.[doi:10.3969/j.issn.1673-4785.201403072]
[3]殷瑞,苏松志,李绍滋.一种卷积神经网络的图像矩正则化策略[J].智能系统学报,2016,11(1):43.[doi:10.11992/tis.201509018]
　YIN Rui,SU Songzhi,LI Shaozi.Convolutional neural network’s image moment regularizing strategy[J].CAAI Transactions on Intelligent Systems,2016,11():43.[doi:10.11992/tis.201509018]
[4]马晓,张番栋,封举富.基于深度学习特征的稀疏表示的人脸识别方法[J].智能系统学报,2016,11(3):279.[doi:10.11992/tis.201603026]
　MA Xiao,ZHANG Fandong,FENG Jufu.Sparse representation via deep learning features based face recognition method[J].CAAI Transactions on Intelligent Systems,2016,11():279.[doi:10.11992/tis.201603026]
[5]龚震霆,陈光喜,任夏荔,等.基于卷积神经网络和哈希编码的图像检索方法[J].智能系统学报,2016,11(3):391.[doi:10.11992/tis.201603028]
　GONG Zhenting,CHEN Guangxi,REN Xiali,et al.An image retrieval method based on a convolutional neural network and hash coding[J].CAAI Transactions on Intelligent Systems,2016,11():391.[doi:10.11992/tis.201603028]
[6]师亚亭,李卫军,宁欣,等.基于嘴巴状态约束的人脸特征点定位算法[J].智能系统学报,2016,11(5):578.[doi:10.11992/tis.201602006]
　SHI Yating,LI Weijun,NING Xin,et al.A facial feature point locating algorithmbased on mouth-state constraints[J].CAAI Transactions on Intelligent Systems,2016,11():578.[doi:10.11992/tis.201602006]
[7]马世龙,乌尼日其其格,李小平.大数据与深度学习综述[J].智能系统学报,2016,11(6):728.[doi:10.11992/tis.201611021]
　MA Shilong,WUNIRI Qiqige,LI Xiaoping.Deep learning with big data: state of the art and development[J].CAAI Transactions on Intelligent Systems,2016,11():728.[doi:10.11992/tis.201611021]
[8]王亚杰,邱虹坤,吴燕燕,等.计算机博弈的研究与发展[J].智能系统学报,2016,11(6):788.[doi:10.11992/tis.201609006]
　WANG Yajie,QIU Hongkun,WU Yanyan,et al.Research and development of computer games[J].CAAI Transactions on Intelligent Systems,2016,11():788.[doi:10.11992/tis.201609006]
[9]黄心汉.A3I:21世纪科技之光[J].智能系统学报,2016,11(6):835.[doi:10.11992/tis.201605022]
　HUANG Xinhan.A3I: the star of science and technology for the 21st century[J].CAAI Transactions on Intelligent Systems,2016,11():835.[doi:10.11992/tis.201605022]
[10]杨晓兰,强彦,赵涓涓,等.基于医学征象和卷积神经网络的肺结节CT图像哈希检索[J].智能系统学报,2017,12(6):857.[doi:10.11992/tis.201706035]
　YANG Xiaolan,QIANG Yan,ZHAO Juanjuan,et al.Hashing retrieval for CT images of pulmonary nodules based on medical signs and convolutional neural networks[J].CAAI Transactions on Intelligent Systems,2017,12():857.[doi:10.11992/tis.201706035]
[11]刘帅师,程曦,郭文燕,等.深度学习方法研究新进展[J].智能系统学报,2016,11(5):567.[doi:10.11992/tis.201511028]
　LIU Shuaishi,CHENG Xi,GUO Wenyan,et al.Progress report on new research in deep learning[J].CAAI Transactions on Intelligent Systems,2016,11():567.[doi:10.11992/tis.201511028]
[12]宋婉茹,赵晴晴,陈昌红,等.行人重识别研究综述[J].智能系统学报,2017,12(6):770.[doi:10.11992/tis.201706084]
　SONG Wanru,ZHAO Qingqing,CHEN Changhong,et al.Survey on pedestrian re-identification research[J].CAAI Transactions on Intelligent Systems,2017,12():770.[doi:10.11992/tis.201706084]
[13]王科俊,赵彦东,邢向磊.深度学习在无人驾驶汽车领域应用的研究进展[J].智能系统学报,2018,13(1):55.[doi:10.11992/tis.201609029]
　WANG Kejun,ZHAO Yandong,XING Xianglei.Deep learning in driverless vehicles[J].CAAI Transactions on Intelligent Systems,2018,13():55.[doi:10.11992/tis.201609029]
[14]莫凌飞,蒋红亮,李煊鹏.基于深度学习的视频预测研究综述[J].智能系统学报,2018,13(1):85.[doi:10.11992/tis.201707032]
　MO Lingfei,JIANG Hongliang,LI Xuanpeng.Review of deep learning-based video prediction[J].CAAI Transactions on Intelligent Systems,2018,13():85.[doi:10.11992/tis.201707032]
[15]葛园园,许有疆,赵帅,等.自动驾驶场景下小且密集的交通标志检测[J].智能系统学报,2018,13(3):366.[doi:10.11992/tis.201706040]
　GE Yuanyuan,XU Youjiang,ZHAO Shuai,et al.Detection of small and dense traffic signs in self-driving scenarios[J].CAAI Transactions on Intelligent Systems,2018,13():366.[doi:10.11992/tis.201706040]
[16]汪鸿翔,柳培忠,骆炎民,等.高斯核函数卷积神经网络跟踪算法[J].智能系统学报,2018,13(3):388.[doi:10.11992/tis.201612040]
　WANG Hongxiang,LIU Peizhong,LUO Yanmin,et al.Convolutional neutral network tracking algorithm accelerated by Gaussian kernel function[J].CAAI Transactions on Intelligent Systems,2018,13():388.[doi:10.11992/tis.201612040]
[17]冯小荣,惠康华,柳振东.基于卷积特征和贝叶斯分类器的人脸识别[J].智能系统学报,2018,13(5):769.[doi:10.11992/tis.201706052]
　FENG Xiaorong,HUI Kanghua,LIU Zhendong.Face recognition based on convolution feature and Bayes classifier[J].CAAI Transactions on Intelligent Systems,2018,13():769.[doi:10.11992/tis.201706052]
[18]莫宏伟,汪海波.基于Faster R-CNN的人体行为检测研究[J].智能系统学报,2018,13(6):967.[doi:10.11992/tis.201801025]
　MO Hongwei,WANG Haibo.Research on human behavior detection based on Faster R-CNN[J].CAAI Transactions on Intelligent Systems,2018,13():967.[doi:10.11992/tis.201801025]
[19]胡越,罗东阳,花奎,等.关于深度学习的综述与讨论[J].智能系统学报,2019,14(1):1.[doi:10.11992/tis.201808019]
　HU Yue,LUO Dongyang,HUA Kui,et al.Overview on deep learning[J].CAAI Transactions on Intelligent Systems,2019,14():1.[doi:10.11992/tis.201808019]
[20]孙必慎,石武祯,姜峰.计算视觉核心问题：自然图像先验建模研究综述[J].智能系统学报,2019,14(1):71.[doi:10.11992/tis.201804019]
　SUN Bishen,SHI Wuzhen,JIANG Feng.Core problem in computer vision: survey of natural image prior models[J].CAAI Transactions on Intelligent Systems,2019,14():71.[doi:10.11992/tis.201804019]

备注/Memo

收稿日期:2024-9-19。
基金项目:国家自然科学基金项目(62201468).
作者简介:田春伟，教授，博士生导师，主要研究方向为图像复原和识别、图像生成。发表学术论文80余篇，7篇ESI高被引论文、3篇ESI热点论文、4篇顶刊封面论文、1篇国际模式识别会刊Pattern Recognition的Best Paper Award、1项中国图象图形学学会自然科学奖二等奖(排名第1)、广东省自然科学奖一等奖。E-mail：chunweitian@hit.edu.cn。;张艳宁，教授，博士生导师，主要研究方向为图像处理、模式识别、计算机视觉与智能信息处理。承担国家重点基础研究发展计划项目、国家自然科学基金重点项目等国家级项目40余项，发表学术论文百余篇。E-mail: ynzhang@nwpu.edu.cn。;张师超，教授，博士生导师，主要研究方向为数据挖掘、机器学习。主持国家自然科学基金、国家重点基础研究发展计划项目、国家高技术研究发展计划项目、澳大利亚ARC(Australian Research Council)国家级项目共12项，主持省部级项目10余项。发表学术论文270余篇。E-mail：zhangsc@mailbox.gxnu.edu.cn。
通讯作者:张师超. E-mail：zhangsc@mailbox.gxnu.edu.cn

更新日期/Last Update: 1900-01-01

卷积神经网络在图像超分辨上的应用 PDF下载HTML

备注/Memo

卷积神经网络在图像超分辨上的应用

PDF下载 HTML