Optical Technique, Volume. 48, Issue 3, 357(2022)

Super-resolution reconstruction of single image based on multilevel attention dense residual network

YUAN Ming, LI Fan, LI Huafeng, and ZHANG Yafei
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    References(28)

    [1] [1] L Zhang, X Wu. An edge-guided image interpolation algorithm via directional filtering and data fusion[J]. IEEE Transactions on Image Processing,2006,15(8):2226-2238.

    [2] [2] William T Freeman, Egon C Pasztor, Owen T Carmichael. Learning low-level vision[J]. International Journal of Computer Vision,2000,2(1):25-47.

    [3] [3] Chang H, Yeung D Y, Xiong Y. Super-resolution through neighbor embedding[C]∥In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington,USA:IEEE,2004(1):I-I.

    [4] [4] Yang J, Wright J, Ma Y. Image super-resolution as sparse representation of raw image patches[C]∥ Computer Vision and Pattern Recognition. Anchorage, USA:IEEE,2008:1-8.

    [5] [5] Dong C, Loy C C, He K, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,38(2):295-307.

    [6] [6] Chao Dong, Chen Change Loy, Xiaoou Tang. Accelerating the Super-resolution convolutional neural network[C]∥ European Conference on Computer Vision. Springer, Cham,2016:3.

    [7] [7] Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C]∥Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition.Caesars Palace in Las Vegas,USA:IEEE,2016:1646-1654.

    [8] [8] Kim J W, Lee J K, et al. Deeply-recursive convolutional network for image Super-resolution[C]∥ IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas,USA:IEEE,2016:1637-1645.

    [9] [9] Lai W S, Huang J B, Ahuja N, et al. Deep laplacian pyramid networks for fast and accurate super-resolution[C]∥Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition. Hawaii,USA:IEEE,2017:624-632.

    [10] [10] Zhang Y, Tian Y, Kong Y, et al. Residual dense network for image super-resolution[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City,USA:IEEE,2018:2472-2481.

    [11] [11] Lim B, Son S, Kim H, et al. Enhanced deep residual networks for single image super-resolution[C]∥Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition Workshops,2017:136-144.

    [12] [12] Ledig C, Theis L, Huszár F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]∥Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition.Hawaii,USA:IEEE,2017:4681-4690.

    [13] [13] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]∥Proceedings of the IEEE conference on computer vision and pattern recognition. Hawaii,USA:IEEE,2017:4700-4708.

    [14] [14] Wang C, Li Z, Shi J. Lightweight image Super-resolution with adaptive weighted learning network[J]. arXiv preprint arXiv:1904.02358,2019.

    [15] [15] Szegedy C, Liu W, Jia Y, et al. Going Deeper with Convolutions[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition.Massachusetts,USA:IEEE,2015:1-9.

    [16] [16] Huang Gao, Chen Danlu, Li Tianhong et al. Multi-scale dense networks for resource efficient image classification[C]∥ International Conference on Learning Representations,.Salt Lake City,USA:IEEE,2018.

    [17] [17] Nah S, Kim T H, Lee K M. Deep Multi-scale convolutional neural network for dynamic scene deblurring[C]∥ 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Hawaii,USA:IEEE,2017.

    [18] [18] Hu Y, Gao X, Li J, et al. Single image super-resolution via cascaded multi-scale cross network[J]. arXiv preprint arXiv:1802.08808,2018.

    [19] [19] Gao S, Zhuang X. Multi-scale deep neural networks for real image super-resolution[C]∥Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition Workshops. Long Beach,USA:IEEE,2019:3.

    [21] [21] Xie T, Yang X, Jia Y, et al. Adaptive densely connected single image Super-resolution[C]∥2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). COEX,,Korea:IEEE,2019:3432-3440.

    [22] [22] Lyn J. Multi-level feature fusion mechanism for single image super-resolution[J]. arXiv preprint arXiv:2002.05962,2020.

    [23] [23] Huafeng Li, Yitang Wang, Zhao Yang, et al. Discriminative dictionary Learning-based multiple component decomposition for Detail-preserving noisy image fusion[J]. IEEE Transactions on Instrumentation and Measurement,2020,69(4):1082-1102.

    [24] [24] Huafeng Li, Shuanglin Yan, Zhengtao Yu, et al. Attribute-identity embedding and Self-supervised learning for scalable person re-identification[C]∥ IEEE Transactions on Circuits and Systems and Systems for Video Techonlogy,Tcsvt,2020:2.

    [25] [25] Huafeng Li, Xiaoge He, Dapeng Tao, et al. Joint medical image fusion, denoising and enhancement via discriminative low-rank sparse dictionaries learning[J]. Pattern Recognition,2018,79:130-146.

    [26] [26] Huafeng Li, Xiaoge He, Zhengtao Yu, et al. Noise-robust image fusion with low-rank sparse decomposition guided by external patch prior[J]. Information Sciences,2020,523:10-24.

    [27] [27] El-Khamy S E, Hadhoud M M, Dessouky M I, et al. Wavelet fusion: A tool to break the limits on LMMSE image super-resolution[J]. International Journal of Wavelets, Multiresolution and Information Processing,2006,4(01):105-118.

    [28] [28] Ji H, Fermüller C. Robust wavelet-based super-resolution reconstruction: theory and algorithm[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,31(4):649-660.

    [29] [29] Xiao C, Yu J, Su K. Gibbs artifact reduction for POCS super-resolution image reconstruction[J]. Frontiers of Computer Science in China,2008,2(1):87-93.

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    YUAN Ming, LI Fan, LI Huafeng, ZHANG Yafei. Super-resolution reconstruction of single image based on multilevel attention dense residual network[J]. Optical Technique, 2022, 48(3): 357

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    Paper Information

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    Received: Nov. 9, 2021

    Accepted: --

    Published Online: Jan. 20, 2023

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    CSTR:32186.14.

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