Skip to main content
SpringerLink
Log in

A Comparison of Feature-Combination for Example-Based Super Resolution

  • Conference paper
  • First Online:
Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8643))

Included in the following conference series:

  • 2151 Accesses

Abstract

Super resolution (SR) in computer vision is an important task. In this paper, we compared several common used features in image super resolution of example-based algorithms. To combine features, we develop a cascade framework to both solve the problem of deciding weights among features and to improve computation efficiency. Finally, we modify the framework to have an adaptive threshold such that not only the computation load is much reduced but the modified framework is suitable to any query image as well as various image databases.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Huang, T.S., Tsai, R.Y.: Multi-frame image restoration and registration. Adv. Comput. Vis. Image Process. 1, 317–339 (1984)

    Google Scholar 

  2. Sun, J., Xu, Z., Shum, H.-Y.: Steinke: image super-resolution using gradient profile prior. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8, 23–28 June 2008

    Google Scholar 

  3. Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. In: Proceedings of the IEEE Computer Society of Conference on Computer Vision Pattern Recogonition, pp. 275–282 (2004)

    Google Scholar 

  4. Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. Comp. Graph. Appl. 22(2), 56–65 (2002)

    Article  Google Scholar 

  5. Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: Proceedings of ICCV (2009), pp. 349–356 (2009)

    Google Scholar 

  6. http://software.intel.com/sites/products/documentation/hpc/ipp/ippi/ippi_appendices/Images/ippi_appB_2.jpg. Downloaded on June 2013

Download references

Author information

Authors and Affiliations

  1. PRIA. Laboratory, Department of Computer Science and Information Engineering, Tamkang University, New Taipei, Taiwan, Republic of China

    Shwu-Huey Yen, Jen-Hui Tsao & Wan-Ting Liao

Authors
  1. Shwu-Huey Yen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jen-Hui Tsao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wan-Ting Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shwu-Huey Yen .

Editor information

Editors and Affiliations

  1. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

  2. Google Research, Mountain View, California, USA

    Haixun Wang

  3. University of Melbourne, Melbourne, Victoria, Australia

    James Bailey

  4. National Cheng Kung University, Tainan, Taiwan

    Vincent S. Tseng

  5. Japan Advanced Institute of Science and Technology, Nomi City, Japan

    Tu Bao Ho

  6. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  7. National Chengchi University, Taipei, Taiwan

    Arbee L.P. Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Yen, SH., Tsao, JH., Liao, WT. (2014). A Comparison of Feature-Combination for Example-Based Super Resolution. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_66

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-13186-3_66

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13185-6

  • Online ISBN: 978-3-319-13186-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation