Skip to main content
SpringerLink
Log in

Noisy Image Fusion Based on a Neural Network with Linearly Constrained Least Square Optimization

  • Conference paper
  • First Online:
Advances in Neural Networks – ISNN 2015 (ISNN 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9377))

Included in the following conference series:

  • 2387 Accesses

Abstract

Image fusion algorithm is a key technology to eliminate noise through combining each image with different weight. Recently, convergence and convergence speed are two exiting problems which attract more and more attention. In this paper, we originally propose a image fusion algorithm based on neural network. Firstly, the linearly constrained least square(LCLS) model which can deal with image fusion problem is introduced. In addition, in order to handle LCLS model, we adopt the penalty function technique to construct a neural network. The proposed algorithm has a simpler structure and faster convergence speed. Lastly, simulation results show this fusion algorithm which has great ability to remove different noise.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Mammone, J.R., Podilchuk, C.I.: A general iterative method of image recovery. In: Visual Communications and Image Processing II (1987)

    Google Scholar 

  2. Katsaggelos, A.K., et al.: A regularized iterative image restoration algorithm. IEEE Transactions on Signal Processing 39, 914–929 (1991)

    Article  Google Scholar 

  3. Sun, Y.: Hopfield neural network based algorithms for image restoration and reconstruction. I. Algorithms and simulations. IEEE Transactions on Signal Processing 48, 2105–2118 (2000)

    Article  MATH  Google Scholar 

  4. Thompson, A.M., et al.: A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization. Ranaon on Arn Analy and Mahn Nllgn 13, 326–339 (1991)

    Google Scholar 

  5. Galatsanos, N.P., Katsaggelos, A.K.: Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation. IEEE Transactions on Image Processing 1, 322–336 (1992)

    Article  Google Scholar 

  6. Kang, M.G., Katsaggelos, A.K.: General choice of the regularization functional in regularized image restoration. IEEE Transactions on Image Processing 4, 594–602 (1995)

    Article  Google Scholar 

  7. Tekalp, A., et al.: Identification of image and blur parameters for the restoration of non- causal blurs. IEEE Transactions on Speech Signal Process 34, 963–972 (1986)

    Article  Google Scholar 

  8. Hou, Z., et al.: Image denoising using robust regression. IEEE Signal Processing Letters 11, 243–246 (2004)

    Article  Google Scholar 

  9. Zervakis, M.E., et al.: A Class Of Robust Entropic Functionals For Image Restoration. IEEE Transactions on Image Processing 4, 752–773 (1995)

    Article  Google Scholar 

  10. Reeves, S.J., Mersereau, R.M.: Blur identification by the method of generalized cross-validation. IEEE Transactions on Image Processing 1, 301–311 (1992)

    Article  Google Scholar 

  11. Hong, M.-C., et al.: Iterative Regularized Least-Mean Mixed-Norm Image Restoration. Opt. Eng. 41, 2515–2524 (2002)

    Article  Google Scholar 

  12. Yan, Z., et al.: A collective neurodynamic optimization approach to bound-constrained nonconvex optimization. Neural Networks 55, 20–29 (2014)

    Article  MATH  Google Scholar 

  13. Xia, Y., Leung, H., Boss\(\acute{e}\), E.: Neural data fusion algorithms based on a linearly constrained least square method. IEEE Transactions on Neural Networks 13, 320–329 (2002)

    Google Scholar 

  14. Xia, Y., Leung, H.: Performance analysis of statistical optimal data fusion algorithms. Information Sciences 277, 808–824 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  15. Xia, Y., Mohamed, S.K.: Novel cooperative neural fusion algorithms for image restoration and image fusion. IEEE Transactions on Image Processing 16, 367–381 (2007)

    Article  MathSciNet  Google Scholar 

  16. Xia, Y., Leung, H.: A Fast Learning Algorithm for Blind Data Fusion Using a Novel L 2 -Norm Estimation. IEEE Sensors Journal 3, 666–672 (2014)

    Article  Google Scholar 

  17. Xia, Y., Wang, J.: A general methodology for designing globally convergent optimization neural networks. IEEE Transactions on Neural Networks 9, 1331–1343 (1998)

    Article  Google Scholar 

  18. Luenberger, D.G., Ye, Y.: Linear and nonlinear programming. Springer Science and Business Media 116 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Southwest University, Beibei, 400715, Chongqing, P.R. China

    Xiaojuan Liu, Lidan Wang & Shukai Duan

Authors
  1. Xiaojuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lidan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shukai Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaojuan Liu .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Xiaolin Hu

  2. Fuzhou University, Fuzhou, China

    Yousheng Xia

  3. School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China

    Yunong Zhang

  4. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Dongbin Zhao

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Liu, X., Wang, L., Duan, S. (2015). Noisy Image Fusion Based on a Neural Network with Linearly Constrained Least Square Optimization. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds) Advances in Neural Networks – ISNN 2015. ISNN 2015. Lecture Notes in Computer Science(), vol 9377. Springer, Cham. https://doi.org/10.1007/978-3-319-25393-0_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25393-0_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25392-3

  • Online ISBN: 978-3-319-25393-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation