Skip to main content
SpringerLink
Log in

Pixel attention convolutional network for image super-resolution

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN). Single-image super-resolution reconstruction technology is to reconstruct fuzzy low-resolution images into clearer high-resolution images. It is a research hotspot in the field of computer vision and image processing. In recent years, the attention mechanism has been successfully applied in image super-resolution reconstruction. However, the existing methods use the channel attention mechanism and the spatial attention mechanism separately, or simply superimpose them, which cannot effectively unify the adjustment effects of both, and the performance is limited. This paper proposes a method that can merge channel attention and spatial attention into pixel attention, which achieves more precise adjustment of feature map information. The pixel attention convolutional neural network method built on this basis can improve the quality of image texture detail reconstruction. We have been tested on five widely used standard datasets, the experimental results show that the method is superior to most current representative reconstruction methods, especially in terms of high-definition picture texture restoration.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Allebach J, Wong PW (1996) Edge-directed interpolation. In: Proceedings of 3rd IEEE international conference on image processing. vol 3, pp 707–710. IEEE

  2. Che Aminudin MF, Suandi SA (2021) Video surveillance image enhancement via a convolutional neural network and stacked denoising autoencoder. Neural Comput Appl 34(4):3079–3095

    Article  Google Scholar 

  3. Dai T, Cai J, Zhang Y, Xia ST, Zhang L (2019) Second-order attention network for single image super-resolution. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp 11065–11074

  4. Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. In: European conference on computer vision. Springer pp 184–199

  5. Dong W, Zhang L, Shi G, Wu X (2011) Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans Image Process 20(7):1838–1857

    Article  MathSciNet  MATH  Google Scholar 

  6. Farooq M, Dailey MN, Mahmood A, Moonrinta J, Ekpanyapong M (2021) Human face super-resolution on poor quality surveillance video footage. Neural Comput Appl 33(20):13505–13523

    Article  Google Scholar 

  7. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770–778

  8. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7132–7141

  9. Hu Y, Li J, Huang Y, Gao X (2019) Channel-wise and spatial feature modulation network for single image super-resolution. IEEE Trans Circuits Syst Video Technol 30(11):3911–3927

    Article  Google Scholar 

  10. Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1646–1654

  11. Kim J, Lee JK, Lee KM (2016) Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1637–1645

  12. Kim JH, Choi JH, Cheon M, Lee JS (2018) Ram: Residual attention module for single image super-resolution. arXiv preprint arXiv:1811.12043

  13. Lai WS, Huang JB, Ahuja N, Yang MH (2017) Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 624–632

  14. Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z et al. (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 4681–4690

  15. Li J, Fang F, Mei K, Zhang G (2018) Multi-scale residual network for image super-resolution. In: Proceedings of the European Conference on Computer Vision (ECCV). pp 517–532

  16. Lim B, Son S, Kim H, Nah S, Mu Lee K (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp 136–144

  17. Mei Y, Fan Y, Zhou Y (2021) Image super-resolution with non-local sparse attention. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp 3517–3526

  18. Niu B, Wen W, Ren W, Zhang X, Yang L, Wang S, Zhang K, Cao X, Shen H (2020) Single image super-resolution via a holistic attention network. In: European Conference on Computer Vision. Springer pp 191–207

  19. Ren S, Guo K, Ma J, Zhu F, Hu B, Zhou H (2021) Realistic medical image super-resolution with pyramidal feature multi-distillation networks for intelligent healthcare systems. Neural Comput Appl pp 1–16

  20. Shamsolmoali P, Celebi ME, Wang R (2020) Deep learning approaches for real-time image super-resolution. Neural Comput Appl 32(18):14519–14520

    Article  Google Scholar 

  21. Shi W, Caballero J, Huszár F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1874–1883

  22. Tai Y, Yang J, Liu X, Xu C (2017) Memnet: A persistent memory network for image restoration. In: Proceedings of the IEEE international conference on computer vision. pp 4539–4547

  23. Timofte R, Rothe R, Van Gool L (2016) Seven ways to improve example-based single image super resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1865–1873

  24. Tong T, Li G, Liu X, Gao Q (2017) Image super-resolution using dense skip connections. In: Proceedings of the IEEE international conference on computer vision. pp 4799–4807

  25. Wang W, Guo R, Tian Y, Yang W (2019) Cfsnet: Toward a controllable feature space for image restoration. In: Proceedings of the IEEE/CVF international conference on computer vision. pp 4140–4149

  26. Wang X, Gu Y, Gao X, Hui Z (2019) Dual residual attention module network for single image super resolution. Neurocomputing 364:269–279

    Article  Google Scholar 

  27. Woo S, Park J, Lee JY, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV). pp 3–19

  28. Yang CY, Yang MH (2013) Fast direct super-resolution by simple functions. In: Proceedings of the IEEE international conference on computer vision. pp 561–568

  29. Yang F, Yang H, Fu J, Lu H, Guo B (2020) Learning texture transformer network for image super-resolution. In: Proceedings of the IEEE/cvf conference on computer vision and pattern recognition. pp 5791–5800

  30. Zhang L, Wu X (2006) An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans Image Process 15(8):2226–2238

    Article  Google Scholar 

  31. Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y (2018) Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European conference on computer vision (ECCV). pp 286–301

  32. Zhang Y, Tian Y, Kong Y, Zhong B, Fu, Y (2018) Residual dense network for image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2472–2481

  33. Zhao H, Kong X, He J, Qiao Y, Dong C (2020) Efficient image super-resolution using pixel attention. Springer, pp 56–72

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Nos.61901099 and 61876205)

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Shufen Zhang & Jiale Zhang

  2. School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, China

    Xin Wang, Yuanyuan Lin & Yanxia Lyu

Authors
  1. Xin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shufen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuanyuan Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yanxia Lyu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiale Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanxia Lyu.

Ethics declarations

Conflict of interest

All the authors declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Zhang, S., Lin, Y. et al. Pixel attention convolutional network for image super-resolution. Neural Comput & Applic 35, 8589–8599 (2023). https://doi.org/10.1007/s00521-022-08132-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-08132-1

Keywords

Search

Navigation