SpringerLink
Log in

Light Field Image Super-Resolution via Global-View Information Adaption and Angular Attention Fusion

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1965))

Included in the following conference series:

  • 563 Accesses

Abstract

Light field (LF) is a emerging technology, which can be used in many fields. Furthermore, LF cameras can capture spatial and angular information of 3D real-world scenes. This information is beneficial for image super-resolution (SR). However, most existing LF approaches have the limitation of utilizing the global-view information, which contains the correlation information among all LF. Moreover, to exploit the complementary information from different views of an LF image, we propose a novel SR method that adapts each view to a global domain with the guidance of global-view information. Our method, called LF-GAGNet, uses a dual-branch network to align features across views with deformable convolutions and fuse them with an attention mechanism. The upper branch extracts global-view information and generates adaptive guidance factors for each view through a global-view adaptation-guided module (GAGM). The lower branch uses these factors as offsets for deformable convolutions to achieve feature alignment in the global domain. Furthermore, we design an angular attention fusion module (AAFM) to enhance the angular features of each view according to their importance. We evaluate our method on various real-world scenarios and show that it surpasses other state-of-the-art methods in terms of SR quality and performance. We also demonstrate that our method can handle complex realistic LF scenarios effectively.

Supported by Macao Polytechnic University.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 74.89
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 96.29
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Zhu, H., Wang, Q., Yu, J.: Occlusion-model guided antiocclusion depth estimation in light field. IEEE J. Sel. Topics Signal Process. 11(7), 965–978 (2017)

    Article  Google Scholar 

  2. Zhang, M., et al.: Memory-oriented decoder for light field salient object detection. In: NeurIPS 2019, pp. 896–906 (2019)

    Google Scholar 

  3. Zhang, S., Lin, Y., Sheng, H.: Residual networks for light field image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11046–11055 (2019)

    Google Scholar 

  4. **, J., Hou, J., Chen, J., Kwong, S.: Light field spatial super-resolution via deep combinatorial geometry embedding and structural consistency regularization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2260–2269 (2020)

    Google Scholar 

  5. Y. Wang, L. Wang, J. Yang, W. An, J. Yu, Y. Guo, Spatial-angular interaction for light field image super-resolution, in: European Conference on Computer Vision, Springer, 2020, pp. 290–308

    Google Scholar 

  6. Wang, Y., et al.: Disentangling light fields for super-resolution and disparity estimation. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)

    Google Scholar 

  7. Zhang, W., Ke, W., Yang, D., et al.: Light field super-resolution using complementary-view feature attention [J]. Comput. Vis. Media 9(4), 843–858 (2023)

    Article  Google Scholar 

  8. Cong, R., Sheng, H., Yang, D., Cui, Z., Chen, R.: Exploiting spatial and angular correlations with deep efficient transformers for light field image super-resolution. IEEE Trans. Multimedia (2023)

    Google Scholar 

  9. Wang, S., Zhou, T., Lu, Y., Di, H.: Detail-preserving transformer for light field image super-resolution, ar**v preprint ar**v:2201.00346 (2022)

  10. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

    Google Scholar 

  11. Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)

    Google Scholar 

  12. Lim, B., Son, S.,Kim, H., Nah, S., Mu Lee, K.: 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 (2017)

    Google Scholar 

  13. Yeung, H.W.F., Hou, J., Chen, X., Chen, J., Chen, Z., Chung, Y.Y.: Light field spatial super-resolution using deep efficient spatial-angular separable convolution. IEEE Trans. Image Process. 28(5), 2319–2330 (2018)

    Article  MathSciNet  Google Scholar 

  14. Wang, Y., et al.: Light field image super-resolution using deformable convolution. IEEE Trans. Image Process. 30, 1057–1071 (2020)

    Article  Google Scholar 

  15. Zhang, S., Chang, S., Lin, Y.: End-to-end light field spatial super-resolution network using multiple epipolar geometry. IEEE Trans. Image Process. 30, 5956–5968 (2021)

    Article  Google Scholar 

  16. Liu, G., Yue, H., Wu, J., Yang, J.: Intra-inter view interaction network for light field image super-resolution. IEEE Trans. Multimedia 25, 256–266 (2021)

    Article  Google Scholar 

  17. Wang, X., Chan, K.C., Yu, K., Dong, C., Loy, C.C.: EDVR: video restoration with enhanced deformable convolutional networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2019, pp. 1954–1963 (2019). https://doi.org/10.1109/CVPRW.2019.00247

  18. Rerabek, M., Ebrahimi, T.: New light field image dataset. In: 8th International Conference on Quality of Multimedia Experience (QoMEX), no. CONF (2016)

    Google Scholar 

  19. Le Pendu, M., Jiang, X., Guillemot, C.: Light field inpainting propagation via low rank matrix completion. IEEE Trans. Image Process. 27(4), 1981–1993 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  20. Vaish, V., Adams, A.: The (new) Stanford light field archive, Computer Graphics Laboratory, Stanford University 6 (7) (2008)

    Google Scholar 

  21. Raj, A.S., Lowney, M., Shah, R., Wetzstein, G.: Stanford Lytro light field archive (2016)

    Google Scholar 

  22. Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018)

    Google Scholar 

  23. Liang, Z., Wang, Y., Wang, L., Yang, J., Zhou, S.: Light field image super-resolution with transformers. IEEE Signal Process. Lett. 29, 563–567 (2022)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, SAR, People’s Republic of China

    Wei Zhang & Wei Ke

  2. State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Bei**g, 100191, People’s Republic of China

    Hao Sheng

  3. Technology Department (Chief Engineer Office), China Mobile Group Design Institute Co., Ltd., Bei**g, 100080, People’s Republic of China

    Wei Zhang

Authors
  1. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Ke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hao Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Zhang .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Bei**g, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, W., Ke, W., Sheng, H. (2024). Light Field Image Super-Resolution via Global-View Information Adaption and Angular Attention Fusion. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1965. Springer, Singapore. https://doi.org/10.1007/978-981-99-8145-8_21

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-8145-8_21

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8144-1

  • Online ISBN: 978-981-99-8145-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation