Skip to main content
SpringerLink
Log in

Image Super-Resolution by PSOSEN of Local Receptive Fields Based Extreme Learning Machine

  • Conference paper
  • First Online:
Proceedings of ELM-2015 Volume 2

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 7))

Abstract

Image super-resolution aims at generating high-resolution images from low-resolution inputs. In this paper, we propose a novel learning-based and efficient image super-resolution approach called particle swarm optimization based selective ensemble (PSOSEN) of local receptive fields based extreme learning machine (ELM-LRF). ELM-LRF is locally connected ELM, which can directly process information including strong correlations such as images. PSOSEN is a selective ensemble used to optimize the output of ELM-LRF. This method constructs an end-to-end mapping of which the input is a single low-resolution image and the output is a high resolution image. Experiments show that our method is better in terms of accuracy and speed with different magnification factors compared to the state-of-the-art 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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: a technical overview. Sig. Process. Mag. IEEE 20(3), 21–36 (2003)

    Article  Google Scholar 

  2. Irani, M., Peleg, S.: Improving resolution by image registration. CVGIP: Graphical Models Image Process 53(3), 231–239 (1991)

    Google Scholar 

  3. Deepu, R., Chaudhuri, S.: Generalized interpolation and its application in super-resolution imaging. Image Vis. Comput. 19, 957–969 (2001)

    Article  Google Scholar 

  4. Tao, H., Tang, X., Liu, J., Tian, J.: Superresolution remote sensing image processing algorithm based on wavelet transform and interpolation. Image Process. Pattern Recog. Remote Sens. 4898, 259–263 (2003)

    Article  Google Scholar 

  5. Surapong, L., Bose, N.K.: High resolution image formation from low resolution frames using Delaunay triangulation. Image process. IEEE Transac. 11, 1427–1441 (2002)

    Article  Google Scholar 

  6. Sina, F., Dirk, R.M., Michael, E., Peyman, M.: Fast and robust multiframe super resolution. Image process. IEEE Transac. on 13, 1327–1344 (2004)

    Article  Google Scholar 

  7. Hardie, R.C., Barnard, K.J., Armstrong, E.E.: Joint MAP registration and high-resolution image estimation using a sequence of undersampled images. Image Process. IEEE Transac. on 6, 1621–1633 (1997)

    Google Scholar 

  8. Atsunori, K., Maeda, S., Ishii, S.: Superresolution with compound Markov random fields via the variational EM algorithm. Neural Netw. 22, 1025–1034 (2009)

    Article  Google Scholar 

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

    Google Scholar 

  10. Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning low-level vision. Int. J. Comput. Vision 40, 25–47 (2000)

    Article  MATH  Google Scholar 

  11. Datsenko, D., Elad, M.: Example-based single document image super-resolution: a global MAP approach with outlier rejection. Multidimension Syst. Signal Process. 18, 103–121 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  12. Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. Image Process. IEEE Transac. on 19(11), 2861–2873 (2010)

    Article  MathSciNet  Google Scholar 

  13. An, L., Bhanu, B.: Image super-resolution by extreme learning machine. In: Image processing (ICIP), 2012 19th IEEE International Conference on, pp. 2209–2212 (2012)

    Google Scholar 

  14. Dong, C., Loy, C.C., He, K., Tang, X, Learning a deep convolutional network for image super-resolution. Computer Vision–ECCV 2014, pp. 184–199 (2014)

    Google Scholar 

  15. Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation of raw image patches. In: Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pp. 1–8 (2008)

    Google Scholar 

  16. Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. Curves Surf. 711–730 (2012)

    Google Scholar 

  17. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)

    Article  Google Scholar 

  18. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)

    Article  Google Scholar 

  19. Liu, Y., He, B., Dong, D., Shen, Y., Yan, T., Nian, R., Lendasse, A.: Particle swarm optimization based selective ensemble of online sequential extreme learning machine. Math. Probl. Eng. 2015, 1–10 (2014)

    Google Scholar 

  20. Huang, G.B., Bai, Z., Lekamalage, L., Kasun, C.: Local receptive fields based extreme learning machine. Comput. Intell. Mag., IEEE 10(2), 18–29 (2015)

    Article  Google Scholar 

  21. Kennedy, J., Spears, W.M.: Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator. Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 78–83 (1998)

    Google Scholar 

  22. Huang, G.B., Zhu, Q.Y., Siew, C.K.: “Extreme learning machine: a new learning scheme of feedforward neural networks”, Neural networks, 2004. Proceedings 2004 IEEE International Joint Conference on, vol. 2, (2004)

    Google Scholar 

  23. Huang, G.B., Zhou, H., Ding, X.: Extreme learning machine for regression and multiclass classification. Syst. Man Cybern. Part B: Cybern., IEEE Transac. on 42(2), 513–529 (2012)

    Article  Google Scholar 

  24. Zhou, Z.H., Tang, W.: Selective ensemble of decision trees. Rough Sets, Fuzzy Sets, Data Min. Granular Comput. 2639, 476–483 (2003)

    Article  Google Scholar 

  25. Saxe, A., Koh, P.W., Chen, Z., Bhand, M., Suresh, B., Ng, A.Y.: On random weights and unsupervised feature learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 1089–1096 (2011)

    Google Scholar 

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

    Google Scholar 

  27. Huynh-Thu, Q., Ghanbari, M.: Scope of validity of PSNR in image/video quality assessment. Electron. Lett. 44(13), 800–801 (2008)

    Article  Google Scholar 

Download references

Acknowledgments

This work is partially supported by the Natural Science Foundation of China (41176076, 51075377, 51379198), the High Technology Research and Development Program of China (2006AA09Z231, 2014AA093410).

Author information

Authors and Affiliations

  1. School of Information and Engineering, Ocean University of China, Qingdao, Shandong, China

    Yan Song, Bo He, Yue Shen & Rui Nian

  2. School of Mechatronic Engineering, China Jiliang University, Hangzhou, Zhejiang, China

    Tianhong Yan

Authors
  1. Yan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bo He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yue Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rui Nian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tianhong Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo He .

Editor information

Editors and Affiliations

  1. Institute of Information and Contro, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  2. Nanyang Technological University, Singapore, Singapore

    Kezhi Mao

  3. ECE, U of Windsor, WINDSOR, Ontario, Canada

    Jonathan Wu

  4. Dept of Mechanical and Industrial Engg, University of Iowa, Iowa City, Iowa, USA

    Amaury Lendasse

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Song, Y., He, B., Shen, Y., Nian, R., Yan, T. (2016). Image Super-Resolution by PSOSEN of Local Receptive Fields Based Extreme Learning Machine. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-28373-9_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28373-9_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28372-2

  • Online ISBN: 978-3-319-28373-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation