Skip to main content
SpringerLink
Log in

Degradation Reconstruction Loss: A Perceptual-Oriented Super-Resolution Framework for Multi-downsampling Degradations

  • Conference paper
  • First Online:
Image and Graphics (ICIG 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12890))

Included in the following conference series:

  • 2209 Accesses

Abstract

Recent years have witnessed the great success of deep learning-based single image super-resolution (SISR) methods. However, most of the existing SR methods assume that low-resolution (LR) images are purely bicubic downsampled from high-resolution (HR) images. Once the actual degradation is not bicubic, their outstanding performance is hard to maintain. Although several SR methods have super-resolved LR images with multiple blur kernels and noise levels, they still follow the bicubic downsampling assumption. To address this issue, we propose a novel degradation reconstruction loss (DRL) to capture the degradation-wise differences between HR images and SR images based on a degradation simulator. By involving the proposed degradation simulator and the loss, a perceptual-oriented SR framework for multi-downsampled images is formed. Extensive experimental results demonstrate that our method outperforms the state-of-the-art perceptual-oriented SR methods on both multi-downsampled datasets and bicubic downsampled datasets.

This work was supported in part by the National Natural Science Foundation of China (No. 62071500, No. 61701313).

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 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
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

References

  1. Agustsson, E., Timofte, R.: NTIRE 2017 challenge on single image super-resolution: dataset and study. In: CVPRW, 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 (2012)

    Google Scholar 

  3. Blau, Y., Mechrez, R., Timofte, R., Michaeli, T., Zelnik-Manor, L.: The 2018 PIRM challenge on perceptual image super-resolution. In: ECCV (2018)

    Google Scholar 

  4. Bruna, J., Sprechmann, P., LeCun, Y.: Super-resolution with deep convolutional sufficient statistics. arXiv preprint arXiv:1511.05666 (2015)

  5. Bulat, A., Yang, J., Tzimiropoulos, G.: To learn image super-resolution, use a GAN to learn how to do image degradation first. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 187–202. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_12

    Chapter  Google Scholar 

  6. Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_13

    Chapter  Google Scholar 

  7. Goodfellow, I., et al.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (2014)

    Google Scholar 

  8. Gu, J., Lu, H., Zuo, W., Dong, C.: Blind super-resolution with iterative kernel correction. In: CVPR, pp. 1604–1613 (2019)

    Google Scholar 

  9. Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: NIPS, pp. 6626–6637 (2017)

    Google Scholar 

  10. Horn, R.A., Johnson, C.R.: Matrix Analysis. Cambridge University Press, Cambridge (2012)

    Book  Google Scholar 

  11. Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: CVPR, pp. 5197–5206 (2015)

    Google Scholar 

  12. Jin, Z., Iqbal, M.Z., Bobkov, D., Zou, W., Li, X., Steinbach, E.: A flexible deep CNN framework for image restoration. IEEE Trans. Multimedia 22, 1055–1068 (2019)

    Article  Google Scholar 

  13. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43

    Chapter  Google Scholar 

  14. Jolicoeur-Martineau, A.: The relativistic discriminator: a key element missing from standard GAN. arXiv preprint arXiv:1807.00734 (2018)

  15. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  16. Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep Laplacian pyramid networks for fast and accurate super-resolution. In: CVPR, pp. 624–632 (2017)

    Google Scholar 

  17. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR, pp. 4681–4690 (2017)

    Google Scholar 

  18. Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: CVPRW, pp. 136–144 (2017)

    Google Scholar 

  19. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV, pp. 416–423 (2001)

    Google Scholar 

  20. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  21. Tai, Y., Yang, J., Liu, X., Xu, C.: MemNet: a persistent memory network for image restoration. In: ICCV, pp. 4539–4547 (2017)

    Google Scholar 

  22. Wang, X., Yu, K., Dong, C., Change Loy, C.: Recovering realistic texture in image super-resolution by deep spatial feature transform. In: CVPR, pp. 606–615 (2018)

    Google Scholar 

  23. Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 63–79. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_5

    Chapter  Google Scholar 

  24. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  25. Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: ICCS, pp. 711–730 (2010)

    Google Scholar 

  26. Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In: CVPR, pp. 3262–3271 (2018)

    Google Scholar 

  27. Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 294–310. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_18

    Chapter  Google Scholar 

  28. Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: CVPR, pp. 2472–2481 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Sun Yat-sen University, Shenzhen, 518107, China

    Zongyao He & Zhi Jin

  2. Technical University of Munich, Munich, Germany

    Xiao Xu

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Lei Luo

Authors
  1. Zongyao He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhi Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhi Jin .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

He, Z., Jin, Z., Xu, X., Luo, L. (2021). Degradation Reconstruction Loss: A Perceptual-Oriented Super-Resolution Framework for Multi-downsampling Degradations. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12890. Springer, Cham. https://doi.org/10.1007/978-3-030-87361-5_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-87361-5_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87360-8

  • Online ISBN: 978-3-030-87361-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation