Skip to main content
SpringerLink
Log in

A Lightweight Local-Global Attention Network for Single Image Super-Resolution

  • Conference paper
  • First Online:
Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13846))

Included in the following conference series:

  • 307 Accesses

Abstract

For a given image, the self-attention mechanism aims to capture dependencies for each pixel. It has been proved that the performance of neural networks which employ self-attention is superior in various image processing tasks. However, the performance of self-attention has extensively correlated with the amount of computation. The vast majority of works tend to use local attention to capture local information to reduce the amount of calculation when using self-attention. The ability to capture information from the entire image is easily weakened on this occasion. In this paper, a local-global attention block (LGAB) is proposed to enhance both the local features and global features with low calculation complexity. To verify the performance of LGAB, a lightweight local-global attention network (LGAN) for single image super-resolution (SISR) is proposed and evaluated. Compared with other lightweight state-of-the-arts (SOTAs) of SISR, the superiority of our LGAN is demonstrated by extensive experimental results. The source code can be found at https://github.com/songzijiang/LGAN.

This work was supported in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 21KJA520007, in part by the National Natural Science Foundation of China under Grant 61572341, in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions, in part by Collaborative Innovation Center of Novel Software Technology and Industrialization.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/xindongzhang/ELAN.

References

  1. Agustsson, E., Timofte, R.: NTIRE 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop, pp. 126–135 (2017)

    Google Scholar 

  2. Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: Proceedings of the British Machine Vision Conference, pp. 1–10 (2012)

    Google Scholar 

  3. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2020)

    Google Scholar 

  4. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  5. Hu, Y., Gao, X., Li, J., Huang, Y., Wang, H.: Single image super-resolution with multi-scale information cross-fusion network. Signal Process. 179, 107831 (2021)

    Article  Google Scholar 

  6. Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197–5206 (2015)

    Google Scholar 

  7. Huang, Z., et al.: CCNet: Criss-cross attention for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2020)

    Google Scholar 

  8. Hui, Z., Gao, X., Yang, Y., Wang, X.: Lightweight image super-resolution with information multi-distillation network. In: Proceedings of the ACM International Conference on Multimedia, pp. 2024–2032 (2019)

    Google Scholar 

  9. Hui, Z., Wang, X., Gao, X.: Fast and accurate single image super-resolution via information distillation network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 723–731 (2018)

    Google Scholar 

  10. Ji, J., Zhong, B., Ma, K.K.: Single image super-resolution using asynchronous multi-scale network. IEEE Signal Process. Lett. 28, 1823–1827 (2021)

    Article  Google Scholar 

  11. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)

    Google Scholar 

  12. Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: 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 (2017)

    Google Scholar 

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

    Google Scholar 

  14. Li, W., Zhou, K., Qi, L., Jiang, N., Lu, J., Jia, J.: LAPAR: linearly-assembled pixel-adaptive regression network for single image super-resolution and beyond. In: Advances in Neural Information Processing Systems (2020)

    Google Scholar 

  15. Li, X., Chen, Z.: Single image super-resolution reconstruction based on fusion of internal and external features. Multimed. Tools. Appl. 81, 1–17 (2021)

    Google Scholar 

  16. Li, Y., et al.: Neural architecture search for lightweight non-local networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10297–10306 (2020)

    Google Scholar 

  17. Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: SwinIR: Image restoration using swin transformer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1833–1844 (2021)

    Google Scholar 

  18. Liu, D., Wen, B., Fan, Y., Loy, C.C., Huang, T.S.: Non-local recurrent network for image restoration. In: Advances in Neural Information Processing Systems, pp. 1673–1682 (2018)

    Google Scholar 

  19. Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 10012–10022 (2021)

    Google Scholar 

  20. Lu, T., Wang, Y., Wang, J., Liu, W., Zhang, Y.: Single image super-resolution via multi-scale information polymerization network. IEEE Signal Process. Lett. 28, 1305–1309 (2021)

    Article  Google Scholar 

  21. Luo, X., Xie, Y., Zhang, Y., Qu, Y., Li, C., Fu, Y.: LatticeNet: towards lightweight image super-resolution with lattice block. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12367, pp. 272–289. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58542-6_17

    Chapter  Google Scholar 

  22. Martin, D., Fowlkes, C., Tal, D., Malik, J., et al.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 416–423 (2001)

    Google Scholar 

  23. Matsui, Y., Ito, K., Aramaki, Y., Fujimoto, A., Ogawa, T., Yamasaki, T., Aizawa, K.: Sketch-based manga retrieval using manga109 dataset. Multimed. Tools. Appl. 76, 21811–21838 (2017)

    Article  Google Scholar 

  24. Mei, Y., Fan, Y., Zhou, Y.: Image super-resolution with non-local sparse attention. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3517–3526 (2021)

    Google Scholar 

  25. Qin, J., Liu, F., Liu, K., Jeon, G., Yang, X.: Lightweight hierarchical residual feature fusion network for single-image super-resolution. Neurocomputing 478, 104–123 (2022)

    Google Scholar 

  26. Shi, W., et al.: 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 (2016)

    Google Scholar 

  27. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)

    Google Scholar 

  28. Von Gioi, R.G., Jakubowicz, J., Morel, J.M., Randall, G.: LSD: a line segment detector. Image Process. Line 2, 35–55 (2012)

    Google Scholar 

  29. Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)

    Google Scholar 

  30. Wu, B., et al.: Shift: A zero flop, zero parameter alternative to spatial convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9127–9135 (2018)

    Google Scholar 

  31. Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., et al. (eds.) Curves and Surfaces 2010. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27413-8_47

    Chapter  Google Scholar 

  32. Zhang, X., Zeng, H., Guo, S., Zhang, L.: Efficient long-range attention network for image super-resolution. In: Avidan, S., (eds.) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol. 13677. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19790-1_39

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, 215008, China

    Zijiang Song & Baojiang Zhong

Authors
  1. Zijiang Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Baojiang Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Baojiang Zhong .

Editor information

Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Song, Z., Zhong, B. (2023). A Lightweight Local-Global Attention Network for Single Image Super-Resolution. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13846. Springer, Cham. https://doi.org/10.1007/978-3-031-26351-4_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-26351-4_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26350-7

  • Online ISBN: 978-3-031-26351-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation