• Laser & Optoelectronics Progress
  • Vol. 58, Issue 4, 0410013 (2021)
Zihan Chen1, Haobo Wu2、*, Haodong Pei3、*, Rong Chen1, Jiaxin Hu1, and Hengtong Shi1
Author Affiliations
  • 1Futian Power Supply Bureau, Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 518001, China
  • 2School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710071, China
  • 3Key Laboratory of Intelligent Infrared Perception, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
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    DOI: 10.3788/LOP202158.0410013 Cite this Article Set citation alerts
    Zihan Chen, Haobo Wu, Haodong Pei, Rong Chen, Jiaxin Hu, Hengtong Shi. Image Super-Resolution Reconstruction Method Based on Self-Attention Deep Network[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410013 Copy Citation Text show less
    References

    [1] Yang J C, Wright J, Huang T S et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 19, 2861-2873(2010). http://dl.acm.org/citation.cfm?id=1892463"

    [2] Freeman W T, Jones T R, Pasztor E C. Example-based super-resolution[J]. IEEE Computer Graphics and Applications, 22, 56-65(2002).

    [3] Chang H, Yeung D Y, Xiong Y M. Super-resolution through neighbor embedding[C]∥Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 27 - July 2, 2004, Washington, DC, USA.(2004).

    [4] Dong C, Loy C C, He K M et al. Learning a deep convolutional network for image super-resolution[M]. ∥Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science. Cham: Springer, 8692, 184-199(2014).

    [5] 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).

    [6] 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. Lecture notes in computer science. Cham: Springer, 9906, 391-407(2016).

    [7] Shi W Z, 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 (CVPR), June 27-30, 2016,, 1874-1883(2016).

    [8] Johnson J, Alahi A, Li F F. Perceptual losses for real-time style transfer and super-resolution[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer vision - ECCV 2016. Lecture notes in computer science. Cham: Springer, 9906, 694-711(2016).

    [9] Goodfellow I J, Pouget-Abadie J, Mirza M et al. Generative adversarial nets. [C]∥Proceedings of the 27th International Conference on Neural Information Processing Systems, December 8-13, 2014, Montreal, Quebec, Canada. New York: Curran Associates, 2, 2672-2680(2014).

    [10] 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 (CVPR), July 21-26, 2017, Honolulu, HI, USA., 105-114(2017).

    [11] 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. Lecture notes in computer science. Cham: Springer, 11133, 63-79(2019).

    [12] Yuan P Y, Zhang Y P. Imagesuper-resolution reconstruction method using dual discriminator based on generative adversarial networks[J]. Laser & Optoelectronics Progress, 56, 231010(2019).

    [13] Lu Y, Zhou Y, Jiang Z Q et al. Channel attention and multi-level features fusion for single image super-resolution[C]∥2018 IEEE Visual Communications and Image Processing (VCIP), December 9-12, 2018, Taichung, Taiwan, China.(2018).

    [14] Liu Y, Wang Y C, Li N et al. An attention-based approach for single image super resolution[C]∥2018 24th International Conference on Pattern Recognition (ICPR), August 20-24, 2018, Beijing, China., 2777-2784(2018).

    [15] Zhang Y L, Li K P, Li K et al. Image super-resolution using very deep residual channel attention networks[M]. ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision - ECCV 2018. Lecture notes in computer science. Cham: Springer, 11211, 294-310(2018).

    [16] Xi Z H, Yuan K P. Super-resolution image reconstruction based on residual channel attention and multilevel feature fusion[J]. Laser & Optoelectronics Progress, 57, 041504(2020).

    [17] Liu K W, Ma Y, Xiong H X et al. Medical-image super-resolution reconstruction method based on residual channel attention network[J]. Laser & Optoelectronics Progress, 57, 021014(2020).

    [18] Shocher A, Cohen N, Irani M. Zero-shot super-resolution using deep internal learning[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 3118-3126(2018).

    [19] Bulat A, Yang J, Tzimiropoulos G. To learn image super-resolution, use a GAN to learn how to do image degradation first[M]. ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision - ECCV 2018. Lecture notes in computer science. Cham: Springer, 11210, 187-202(2018).

    [20] Yuan Y, Liu S Y, Zhang J W 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 (CVPRW), June 18-22, 2018, Sal, 814-823(2018).

    [21] Haris M, Shakhnarovich G, Ukita N. Deep back-projection networks for super-resolution[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 1664-1673(2018).

    [22] Lai W S, Huang J B, Ahuja N et al. -08-09)[2020-05-29]. https:∥arxiv., org/abs/1710, 01992(2018).

    [23] 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).

    [24] ChenY, TaiY, Liu XM, et al.FSRNet: end-to-end learning face super-resolution with facial priors[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE Press, 2018: 2492- 2501.

    [25] 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 (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 1637-1645(2016).

    [26] Li J C, Fang F M, Mei K F et al. Multi-scale residual network for image super-resolution[M]. ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision - ECCV 2018. Lecture notes in computer science. Cham: Springer, 11212, 527-542(2018).

    [27] Zhang Y L, Tian Y P, Kong Y et al. Residual dense network for image super-resolution[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 2472-2481(2018).

    [28] Wang X L, Girshick R, Gupta A et al. Non-local neural networks[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 7794-7803(2018).

    [29] Simonyan K. -04-10)[ 2020-05-29]. https:∥arxiv., org/abs/1409, 1556(2015).

    [30] Ye Y X, Shan J, Bruzzone L et al. Robust registration of multimodal remote sensing images based on structural similarity[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 2941-2958(2017).

    Zihan Chen, Haobo Wu, Haodong Pei, Rong Chen, Jiaxin Hu, Hengtong Shi. Image Super-Resolution Reconstruction Method Based on Self-Attention Deep Network[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410013
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