Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Neural Networks

ISNN 2004: Advances in Neural Networks - ISNN 2004 pp 393–398Cite as

Image De-noising Using Cross-Validation Method with RBF Network Representation

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3174))

Abstract

This paper presents a new image de-noising method, which based on the image representation model of radial basis function neural network. In this model, the number and distribution of the centers (which are set to the pixels of the observed image) are fixed, and the model parameters of the image representation are chosen by cross-validation method. Experimental results show that the model can represent the image well, and proposed method can reduce the noises in images without need any noise knowledge in priori.

This is a preview of subscription content, 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 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Castleman, K.R.: Digital Image Processing. Prentice-Hall, Englewood Cliffs (1996)

    Google Scholar 

  2. Sugiyama, M., Ogawa, H.: A Unified Method for Optimizing Linear Image Restoration Filters. Signal Processing 82, 1773–1787 (2002)

    Article  MATH  Google Scholar 

  3. Egmont-Petersen, M., Ridder, D., Handels, H.: Image Processing with Neural Networks - A Review. Pattern Recognition 35, 2279–2301 (2003)

    Article  Google Scholar 

  4. Guo, P., Xing, L.: Blind Image Restoration Based on RBF Neural Networks. In: Proceedings of SPIE, Bellingham WA. SPIE, vol. 5298, pp. 259–266 (2004)

    Google Scholar 

  5. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, London (1995)

    Google Scholar 

  6. Park, J., Sandberg, I.W.: Universal Approximation Using Radial-Basis-Function Networks. Neural Computation 3, 246–257 (1991)

    Article  Google Scholar 

  7. Park, J., Sandberg, I.W.: Approximation and Radial Basis-Function Networks. Neural Computation 5, 305–316 (1993)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Beijing Normal University, Beijing, 100875, P.R.China

    Ping Guo & Hongzhai Li

Authors
  1. Ping Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongzhai Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Guo, P., Li, H. (2004). Image De-noising Using Cross-Validation Method with RBF Network Representation. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_62

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-28648-6_62

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22843-1

  • Online ISBN: 978-3-540-28648-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation