Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Reference-Based Image Super-Resolution with Deformable Attention Transformer

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

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13678))

Included in the following conference series:

Abstract

Reference-based image super-resolution (RefSR) aims to exploit auxiliary reference (Ref) images to super-resolve low-resolution (LR) images. Recently, RefSR has been attracting great attention as it provides an alternative way to surpass single image SR. However, addressing the RefSR problem has two critical challenges: (i) It is difficult to match the correspondence between LR and Ref images when they are significantly different; (ii) How to transfer the relevant texture from Ref images to compensate the details for LR images is very challenging. To address these issues of RefSR, this paper proposes a deformable attention Transformer, namely DATSR, with multiple scales, each of which consists of a texture feature encoder (TFE) module, a reference-based deformable attention (RDA) module and a residual feature aggregation (RFA) module. Specifically, TFE first extracts image transformation (e.g., brightness) insensitive features for LR and Ref images, RDA then can exploit multiple relevant textures to compensate more information for LR features, and RFA lastly aggregates LR features and relevant textures to get a more visually pleasant result. Extensive experiments demonstrate that our DATSR achieves state-of-the-art performance on benchmark datasets quantitatively and qualitatively.

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
$34.99 /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
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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

Similar content being viewed by others

References

  1. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214–223 (2017)

    Google Scholar 

  2. Cao, H., ET AL.: Swin-Unet: unet-like pure transformer for medical image segmentation. arXiv preprint arXiv:2105.05537 (2021)

  3. Chan, K.C., Zhou, S., Xu, X., Loy, C.C.: BasicVSR++: Improving video super-resolution with enhanced propagation and alignment. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5972–5981 (2022)

    Google Scholar 

  4. Dai, J., et al.: Deformable convolutional networks. In: IEEE International Conference on Computer Vision, pp. 764–773 (2017)

    Google Scholar 

  5. Dai, T., Cai, J., Zhang, Y., Xia, S.T., Zhang, L.: Second-order attention network for single image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 11065–11074 (2019)

    Google Scholar 

  6. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2015)

    Article  Google Scholar 

  7. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)

    Google Scholar 

  8. Guo, Y., et al.: Closed-loop matters: dual regression networks for single image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5407–5416 (2020)

    Google Scholar 

  9. Guo, Y., Luo, Y., He, Z., Huang, J., Chen, J.: Hierarchical neural architecture search for single image super-resolution. IEEE Sig. Process. Lett. 27, 1255–1259 (2020)

    Article  Google Scholar 

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

    Google Scholar 

  11. Hui, Z., Li, J., Wang, X., Gao, X.: Learning the non-differentiable optimization for blind super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2093–2102 (2021)

    Google Scholar 

  12. Jiang, Y., Chan, K.C., Wang, X., Loy, C.C., Liu, Z.: Robust reference-based super-resolution via c2-matching. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2103–2112 (2021)

    Google Scholar 

  13. Jo, Y., Kim, S.J.: Practical single-image super-resolution using look-up table. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 691–700 (2021)

    Google Scholar 

  14. Kar, A., Biswas, P.K.: Fast bayesian uncertainty estimation and reduction of batch normalized single image super-resolution network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4957–4966 (2021)

    Google Scholar 

  15. Khrulkov, V., Babenko, A.: Neural side-by-side: predicting human preferences for no-reference super-resolution evaluation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4988–4997 (2021)

    Google Scholar 

  16. Kong, X., Zhao, H., Qiao, Y., Dong, C.: ClassSR: a general framework to accelerate super-resolution networks by data characteristic. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 12016–12025 (2021)

    Google Scholar 

  17. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)

    Google Scholar 

  18. Li, Z., Yang, J., Liu, Z., Yang, X., Jeon, G., Wu, W.: Feedback network for image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3867–3876 (2019)

    Google Scholar 

  19. Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: SwinIR: image restoration using swin transformer. In: IEEE International Conference on Computer Vision Workshops, pp. 1833–1844 (2021)

    Google Scholar 

  20. Liang, J., Lugmayr, A., Zhang, K., Danelljan, M., Van Gool, L., Timofte, R.: Hierarchical conditional flow: a unified framework for image super-resolution and image rescaling. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4076–4085 (2021)

    Google Scholar 

  21. Liang, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: Mutual affine network for spatially variant kernel estimation in blind image super-resolution. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4096–4105 (2021)

    Google Scholar 

  22. Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136–144 (2017)

    Google Scholar 

  23. Liu, J., Zhang, W., Tang, Y., Tang, J., Wu, G.: Residual feature aggregation network for image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2359–2368 (2020)

    Google Scholar 

  24. Liu, Q., Liu, C.: A novel locally linear KNN model for visual recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 9446–9454 (2015)

    Google Scholar 

  25. Liu, Z., et al.: Swin transformer: Hierarchical vision transformer using shifted windows. In: IEEE International Conference on Computer Vision, pp. 10012–10022 (2021)

    Google Scholar 

  26. Liu, Z., et al.: Video Swin transformer. arXiv preprint arXiv:2106.13230 (2021)

  27. Lu, L., Li, W., Tao, X., Lu, J., Jia, J.: MASA-SR: matching acceleration and spatial adaptation for reference-based image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6368–6377 (2021)

    Google Scholar 

  28. Lucas, A., Lopez-Tapia, S., Molina, R., Katsaggelos, A.K.: Generative adversarial networks and perceptual losses for video super-resolution. IEEE Trans. Image Process. 28(7), 3312–3327 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  29. Lugmayr, A., Danelljan, M., Timofte, R.: Ntire 2020 challenge on real-world image super-resolution: Methods and results. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 494–495 (2020)

    Google Scholar 

  30. Matsui, Y., et al.: Sketch-based manga retrieval using manga109 dataset. Multimedia Tools Appl. 76(20), 21811–21838 (2016). https://doi.org/10.1007/s11042-016-4020-z

    Article  Google Scholar 

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

    Google Scholar 

  32. Pesavento, M., Volino, M., Hilton, A.: Attention-based multi-reference learning for image super-resolution. In: IEEE International Conference on Computer Vision, pp. 14697–14706 (2021)

    Google Scholar 

  33. Sajjadi, M.S., Scholkopf, B., Hirsch, M.: EnhanceNet: single image super-resolution through automated texture synthesis. In: IEEE International Conference on Computer Vision, pp. 4491–4500 (2017)

    Google Scholar 

  34. Shim, G., Park, J., Kweon, I.S.: Robust reference-based super-resolution with similarity-aware deformable convolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 8425–8434 (2020)

    Google Scholar 

  35. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)

    Google Scholar 

  36. Song, X., et al.: Channel attention based iterative residual learning for depth map super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5631–5640 (2020)

    Google Scholar 

  37. Sun, L., Hays, J.: Super-resolution from internet-scale scene matching. In: IEEE International Conference on Computational Photography, pp. 1–12 (2012)

    Google Scholar 

  38. Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 9446–9454 (2018)

    Google Scholar 

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

    Google Scholar 

  40. Wang, L., Kim, T.K., Yoon, K.J.: EventSR: from asynchronous events to image reconstruction, restoration, and super-resolution via end-to-end adversarial learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 8315–8325 (2020)

    Google Scholar 

  41. Wang, L., et al.: Exploring sparsity in image super-resolution for efficient inference. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4917–4926 (2021)

    Google Scholar 

  42. Wang, X., Chan, K.C., Yu, K., Dong, C., Change Loy, C.: EDVR: video restoration with enhanced deformable convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)

    Google Scholar 

  43. Wang, X., Xie, L., Dong, C., Shan, Y.: Real-ESRGAN: training real-world blind super-resolution with pure synthetic data. In: IEEE International Conference on Computer Vision, pp. 1905–1914 (2021)

    Google Scholar 

  44. Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: European Conference on Computer Vision Workshops (2018)

    Google Scholar 

  45. Xie, Y., Xiao, J., Sun, M., Yao, C., Huang, K.: Feature representation matters: end-to-end learning for reference-based image super-resolution. In: European Conference on Computer Vision, pp. 230–245 (2020)

    Google Scholar 

  46. Xing, W., Egiazarian, K.: End-to-end learning for joint image demosaicing, denoising and super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3507–3516 (2021)

    Google Scholar 

  47. Yan, X., Zhao, W., Yuan, K., Zhang, R., Li, Z., Cui, S.: Towards content-independent multi-reference super-resolution: adaptive pattern matching and feature aggregation. In: European Conference on Computer Vision, pp. 52–68 (2020)

    Google Scholar 

  48. Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5791–5800 (2020)

    Google Scholar 

  49. Zhang, K., Liang, J., Van Gool, L., Timofte, R.: Designing a practical degradation model for deep blind image super-resolution. In: IEEE Conference on International Conference on Computer Vision, pp. 4791–4800 (2021)

    Google Scholar 

  50. Zhang, K., Liang, J., Van Gool, L., Timofte, R.: Designing a practical degradation model for deep blind image super-resolution. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4791–4800 (2021)

    Google Scholar 

  51. Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608–4622 (2018)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  53. Zhang, W., Liu, Y., Dong, C., Qiao, Y.: RankSRGAN: generative adversarial networks with ranker for image super-resolution. In: IEEE International Conference on Computer Vision, pp. 3096–3105 (2019)

    Google Scholar 

  54. Zhang, Y., Li, K., Li, K., Fu, Y.: MR image super-resolution with squeeze and excitation reasoning attention network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 13425–13434 (2021)

    Google Scholar 

  55. Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: European Conference on Computer Vision, pp. 286–301 (2018)

    Google Scholar 

  56. Zhang, Z., Wang, Z., Lin, Z., Qi, H.: Image super-resolution by neural texture transfer. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 7982–7991 (2019)

    Google Scholar 

  57. Zheng, H., Ji, M., Wang, H., Liu, Y., Fang, L.: CrossNet: an end-to-end reference-based super resolution network using cross-scale warping. In: European Conference on Computer Vision, pp. 88–104 (2018)

    Google Scholar 

  58. Zhou, R., Susstrunk, S.: Kernel modeling super-resolution on real low-resolution images. In: IEEE International Conference on Computer Vision, pp. 2433–2443 (2019)

    Google Scholar 

  59. Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable convnets v2: More deformable, better results. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 9308–9316 (2019)

    Google Scholar 

Download references

Acknowledgements

This work was partly supported by Huawei Fund and the ETH Zürich Fund (OK).

Author information

Authors and Affiliations

  1. Computer Vision Lab, ETH Zürich, Zürich, Switzerland

    Jiezhang Cao, Jingyun Liang, Kai Zhang, Yawei Li, Yulun Zhang, Wenguan Wang & Luc Van Gool

  2. KU Leuven, Leuven, Belgium

    Luc Van Gool

Authors
  1. Jiezhang Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jingyun Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kai Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yawei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yulun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wenguan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Luc Van Gool
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yulun Zhang .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 8844 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 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

Cao, J. et al. (2022). Reference-Based Image Super-Resolution with Deformable Attention Transformer. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13678. Springer, Cham. https://doi.org/10.1007/978-3-031-19797-0_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19797-0_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19796-3

  • Online ISBN: 978-3-031-19797-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation