• Laser & Optoelectronics Progress
  • Vol. 59, Issue 4, 0420002 (2022)
Yuanxue Xin2, Fengting Zhu2, Pengfei Shi1、2、*, Xin Yang2, and Runkang Zhou2
Author Affiliations
  • 1Jiangsu Key Laboratory of Power Transmission & Distribution Equipment Technology, Hohai University, Changzhou , Jiangsu 213022, China
  • 2College of Internet of Things Engineering, Hohai University, Changzhou , Jiangsu 213022, China
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    DOI: 10.3788/LOP202259.0420002 Cite this Article Set citation alerts
    Yuanxue Xin, Fengting Zhu, Pengfei Shi, Xin Yang, Runkang Zhou. Super-Resolution Reconstruction Algorithm of Images Based on Improved Enhanced Super-Resolution Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2022, 59(4): 0420002 Copy Citation Text show less
    References

    [1] Zhu S Y, Zeng B, Liu G H et al. Image interpolation based on non-local geometric similarities[C], 16213035(2015).

    [2] Papyan V, Elad M. Multi-scale patch-based image restoration[J]. IEEE Transactions on Image Processing, 25, 249-261(2016).

    [3] Dong C, Loy C C, Tang X O. Accelerating the super-resolution convolutional neural network[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016, 9906, 391-407(2016).

    [4] Dong C, Loy C C, He K M et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2016).

    [5] Kim J, Lee J K, Lee K M. Deeply-recursive convolutional network for image super-resolution[C], 1637-1645(2016).

    [6] Lai W S, Huang J B, Ahuja N et al. Deep Laplacian pyramid networks for fast and accurate super-resolution[C], 5835-5843(2017).

    [7] Ledig C, Theis L, Huszár F et al. Photo-realistic single image super-resolution using a generative adversarial network[C], 105-114(2017).

    [8] Hu S Y, Wang G D, Zhao Y et al. Image super-resolution network based on dense connection and squeeze module[J]. Laser & Optoelectronics Progress, 56, 201005(2019).

    [9] Wang X T, Yu K, Wu S X et al. ESRGAN: enhanced super-resolution generative adversarial networks[M]. Leal-Taixé L, Roth S. Computer vision-ECCV 2018 workshops, 11133, 63-79(2019).

    [10] Hu X Y, Liu X J, Wang Z C et al. RTSRGAN: real-time super-resolution generative adversarial networks[C], 321-326(2019).

    [11] Dou X Y, Li C Y, Shi Q et al. Super-resolution for hyperspectral remote sensing images based on the 3D attention-SRGAN network[J]. Remote Sensing, 12, 1204(2020).

    [12] Zhang Y L, Tian Y P, Kong Y et al. Residual dense network for image restoration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 2480-2495(2021).

    [13] Shao M W, Zhang W T, Zuo W M et al. Multi-scale generative adversarial inpainting network based on cross-layer attention transfer mechanism[J]. Knowledge-Based Systems, 196, 105778(2020).

    [14] Li Y H, Mu X, Zhu Y L et al. Super resolution image restoration and reconstruction of deep generative countermeasure network[J]. Journal of Xi’an University of Technology, 35, 1-8(2021).

    [15] Peng Y F, Zhang P J, Gao Y et al. Attention fusion generative adversarial network for single-image super-resolution reconstruction[J]. Laser & Optoelectronics Progress, 58, 2010012(2021).

    [16] Chen Z H, Wu H B, Pei H D et al. Image super-resolution reconstruction method based on self-attention deep network[J]. Laser & Optoelectronics Progress, 58, 0410013(2021).

    [17] Zha T B, Luo L, Yang K et al. Image reconstruction algorithm based on improved super-resolution generative adversarial network[J]. Laser & Optoelectronics Progress, 58, 0810005(2021).

    [18] Lim B, Son S, Kim H et al. Enhanced deep residual networks for single image super-resolution[C], 1132-1140(2017).

    [19] Nah S, Kim T H, Lee K M. Deep multi-scale convolutional neural network for dynamic scene deblurring[C], 257-265(2017).

    [20] Jolicoeur-Martineau A. The relativistic discriminator: a key element missing from standard GAN[EB/OL]. https∥arxiv.org/abs/1807.00734

    [21] Wang Q L, Wu B G, Zhu P F et al. ECA-net: efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).

    [22] Radford A, Metz L, Chintala et al. Unsupervised representation learning with deep convolutional generative adversarial networks[C](2016).

    [23] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[C](2015).

    [24] Agustsson E, Timofte R. NTIRE 2017 challenge on single image super-resolution: dataset and study[C], 1122-1131(2017).

    [25] Bevilacqua M, Roumy A, Guillemot C et al. Low-complexity single-image super-resolution based on nonnegative neighbor embedding[C], 135.1-135.10(2012).

    [26] Zeyde R, Elad M, Protter M. On single image scale-up using sparse-representations[M]. Boissonnat J D, Chenin P, Cohen A, et al. Curves and surfaces, 6920, 711-730(2012).

    [27] Martin D, Fowlkes C, Tal D et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C], 416-423(2001).

    Yuanxue Xin, Fengting Zhu, Pengfei Shi, Xin Yang, Runkang Zhou. Super-Resolution Reconstruction Algorithm of Images Based on Improved Enhanced Super-Resolution Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2022, 59(4): 0420002
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