Skip to main content
SpringerLink
Log in

Single Image Super-Resolution Reconstruction Based on Edge-Preserving with External and Internal Gradient Prior Knowledge

  • Conference paper
  • First Online:
Computer Vision – ACCV 2016 Workshops (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10116))

Included in the following conference series:

Abstract

Single image super-resolution (SISR) reconstruction is currently a very fundamental and significant task in image processing. Instead of upscaling the image in spatial domain, we propose a novel SISR method based on edge preserving integrating the external gradient priors by deep learning method (auto-encoder network) and internal gradient priors using non-local total variation (NLTV). The gradient domain effectively reflects the high frequency details and edge information of nature image to some extent. The joint perspective exploits the complementary advantages of external and internal gradient prior knowledge for reconstructing the HR image. The experimental results demonstrate the effectiveness of our approach over several state-of-art SISR methods.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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.

    http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/.

References

  1. Li, X., Orchard, M.T.: New edge-directed interpolation. IEEE Trans. Image Process. 10, 1521–1527 (2001)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Marquina, A., Osher, S.J.: Image super-resolution by TV-regularization and bregman iteration. J. Sci. Comput. 37, 367–382 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  4. Zhang, K., Gao, X., Tao, D., Li, X.: Single image super-resolution with non-local means and steering kernel regression. IEEE Trans. Image Process. 21, 4544–4556 (2012)

    Article  MathSciNet  Google Scholar 

  5. Lin, Z., Shum, H.Y.: Fundamental limits of reconstruction-based super resolution algorithms under local translation. IEEE Trans. Pattern Anal. Mach. Intell. 26, 83–97 (2004)

    Article  Google Scholar 

  6. Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19, 2861–2873 (2010)

    Article  MathSciNet  Google Scholar 

  7. 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). doi:10.1007/978-3-319-10593-2_13

    Google Scholar 

  8. Timofte, R., De Smet, V., Van Gool, L.: A+: adjusted anchored neighborhood regression for fast super-resolution. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9006, pp. 111–126. Springer, Cham (2015). doi:10.1007/978-3-319-16817-3_8

    Google Scholar 

  9. Osher, S., Burger, M., Goldfarb, D., Xu, J., Yin, W.: An iterative regularization method for total variation-based image restoration. Multiscale Model. Simul. 4, 460–489 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  10. Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 60–65. IEEE (2005)

    Google Scholar 

  11. Tai, Y.W., Liu, S., Brown, M.S., Lin, S.: Super resolution using edge prior and single image detail synthesis. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2400–2407. IEEE (2010)

    Google Scholar 

  12. Yang, C.Y., Yang, M.H.: Fast direct super-resolution by simple functions. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 561–568 (2013)

    Google Scholar 

  13. Kim, K.I., Kwon, Y.: Single-image super-resolution using sparse regression and natural image prior. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1127–1133 (2010)

    Article  Google Scholar 

  14. Zhang, K., Gao, X., Li, X., Tao, D.: Partially supervised neighbor embedding for example-based image super-resolution. IEEE J. Sel. Top. Sign. Proces. 5, 230–239 (2011)

    Article  Google Scholar 

  15. Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., Chenin, P., Cohen, A., Gout, C., Lyche, T., Mazure, M.-L., Schumaker, L. (eds.) Curves and Surfaces 2010. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012). doi:10.1007/978-3-642-27413-8_47

    Chapter  Google Scholar 

  16. Timofte, R., Smet, V., Gool, L.: Anchored neighborhood regression for fast example-based super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1920–1927 (2013)

    Google Scholar 

  17. Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. arXiv preprint (2015). arXiv:1511.04587

  18. Timofte, R., Rothe, R., Van Gool, L.: Seven ways to improve example-based single image super resolution. arXiv preprint (2015). arXiv:1511.02228

  19. Sun, J., Xu, Z., Shum, H.Y.: Gradient profile prior and its applications in image super-resolution and enhancement. IEEE Trans. Image Process. 20, 1529–1542 (2011)

    Article  MathSciNet  Google Scholar 

  20. Wang, L., Xiang, S., Meng, G., Wu, H., Pan, C.: Edge-directed single-image super-resolution via adaptive gradient magnitude self-interpolation. IEEE Trans. Circuits Syst. Video Technol. 23, 1289–1299 (2013)

    Article  Google Scholar 

  21. Li, Y., Liu, J., Yang, W., Guo, Z.: Neighborhood regression for edge-preserving image super-resolution. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1201–1205. IEEE (2015)

    Google Scholar 

  22. Si, D., Hu, Y., Gan, Z., Cui, Z., Liu, F.: Edge directed single image super resolution through the learning based gradient regression estimation. In: Zhang, Y.-J. (ed.) ICIG 2015. LNCS, vol. 9218, pp. 226–239. Springer, Cham (2015). doi:10.1007/978-3-319-21963-9_21

    Chapter  Google Scholar 

  23. Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2, 1–127 (2009)

    Article  MATH  Google Scholar 

  24. Zhang, X., Burger, M., Bresson, X., Osher, S.: Bregmanized nonlocal regularization for deconvolution and sparse reconstruction. SIAM J. Imaging Sci. 3, 253–276 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  25. Cho, T.S., Zitnick, C.L., Joshi, N., Kang, S.B., Szeliski, R., Freeman, W.T.: Image restoration by matching gradient distributions. IEEE Trans. Pattern Anal. Mach. Intell. 34, 683–694 (2012)

    Article  Google Scholar 

  26. Gilboa, G., Sochen, N.A., Zeevi, Y.Y.: Regularized shock filters and complex diffusion. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 399–413. Springer, Heidelberg (2002). doi:10.1007/3-540-47969-4_27

    Chapter  Google Scholar 

  27. Liu, W., Li, S.: Sparse representation with morphologic regularizations for single image super-resolution. Sig. Process. 98, 410–422 (2014)

    Article  Google Scholar 

  28. Zhou, Q., Chen, S., Liu, J., Tang, X.: Edge-preserving single image super-resolution. In: Proceedings of the 19th ACM International Conference on Multimedia, pp. 1037–1040. ACM (2011)

    Google Scholar 

  29. Dong, C., Loy, C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38, 295–307 (2016)

    Article  Google Scholar 

  30. Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding (2012)

    Google Scholar 

  31. Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 1, p. 1. IEEE (2004)

    Google Scholar 

  32. He, L., Qi, H., Zaretzki, R.: Beta process joint dictionary learning for coupled feature spaces with application to single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 345–352 (2013)

    Google Scholar 

  33. 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, 600–612 (2004)

    Article  Google Scholar 

Download references

Acknowledgement

The authors would like to sincerely thank J. C. Yang, L. He, R. Timofte and C. Dong et al. for sharing the source codes of the ScSR, BP-JDL, NE+NNLS, NE+LLE, ANR and SRCNN methods. This work was funded by the Chinese National Natural Science Foundation (11331012, 71271204, 11571014).

Author information

Authors and Affiliations

  1. Key Laboratory of Big Data Mining and Knowledge Management, School of Mathematical Science, University of Chinese Academy of Sciences (UCAS), Beijing, China

    Ruxin Wang, Congying Han, Mingqiang Li & Tiande Guo

Authors
  1. Ruxin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Congying Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingqiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tiande Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Congying Han .

Editor information

Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Wang, R., Han, C., Li, M., Guo, T. (2017). Single Image Super-Resolution Reconstruction Based on Edge-Preserving with External and Internal Gradient Prior Knowledge. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10116. Springer, Cham. https://doi.org/10.1007/978-3-319-54407-6_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54407-6_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54406-9

  • Online ISBN: 978-3-319-54407-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation