Skip to main content
SpringerLink
Log in

Image restoration techniques based on fuzzy neural networks

  • Published:
Science in China Series F: Information Sciences Aims and scope Submit manuscript

Abstract

By establishing some suitable partitions of input and output spaces, a novel fuzzy neural network (FNN) which is called selection type FNN is developed. Such a system is a multilayer feedforward neural network, which can be a universal approximator with maximum norm. Based on a family of fuzzy inference rules that are of real senses, a simple and useful inference type FNN is constructed. As a result, the fusion of selection type FNN and inference type FNN results in a novel filter—FNN filter. It is simple in structure. And also it is convenient to design the learning algorithm for structural parameters. Further, FNN filter can efficiently suppress impulse noise superimposed on image and preserve fine image structure, simultaneously. Some examples are simulated to confirm the advantages of FNN filter over other filters, such as median filter and adaptive weighted fuzzy mean (AWFM) filter and so on, in suppression of noises and preservation of image structure.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Castleman, K. R., Digital Image Processing, Beijing: Tsinghua University Press, Prentice Hall, Inc., 1998.

    Google Scholar 

  2. Pitas, I., Venetsanopoulos, A. N., Nonlinear Digital Filters—Principles and Applications, Boston: Kluwer Academic Publishers, 1990.

    MATH  Google Scholar 

  3. Hardie, R. C., Barner, K. E., Rank conditional rank selection filters for signal restoration, IEEE Trans. on Image Processing, 1994, 3: 192–206.

    Article  Google Scholar 

  4. Jeong, B., Lee, Y. H., Design of weighted order statistic filters using the perceptron algorithm, IEEE Trans. on Signal Process, 1994, 42: 3264–3268.

    Article  Google Scholar 

  5. Ko, S. J., Lee, Y. H., Center weighted median filters and their applications to image enhancement, IEEE Trans. on Circuits and Systems, 1991, 38: 984–993.

    Article  Google Scholar 

  6. Kundu, A., Mitra, S. K., Vaidyanathan, P. P., Application of two-dimensional generalized mean filtering for removal of impulse noises from images, IEEE Trans. on Acoust. Speech, Signal Processing, 1984, 32: 600–609.

    Article  Google Scholar 

  7. Yin, L., Astola, J., Neuvo, Y., A new class of nonlinear filters—neural filters, IEEE Trans.on Signal Processing, 1993, 41: 1201–1222.

    Article  MATH  Google Scholar 

  8. Civanlar, M. R., Trussell, H. J., Digital image restoration using fuzzy sets, IEEE Trans. on Acoust. Speech, Signal Processing, 1986, 34: 919–936.

    Article  Google Scholar 

  9. Arakawa, K., Median filter based on fuzzy rules and its application to image processing, Fuzzy Sets and Systems, 1996, 77: 3–13.

    Article  Google Scholar 

  10. Chen, R. C., Yu, P. T., Fuzzy selection filters for image restoration with neural learning, IEEE Trans. on Signal Processing, 1999, 47: 1446–1450.

    Article  Google Scholar 

  11. Chen, R. C., Yu, P. T., Fuzzy stack filters—their definition, fundamental properties and application in image processing, IEEE Trans on Image Processing, 1996, 5: 838–854.

    Article  MathSciNet  Google Scholar 

  12. Farbiz, F., Menhaj, M. B., Motamedi, S. A. et al., A new fuzzy logic filter for image enhancement, IEEE Trans. on Systems Man. Cybernet-Part B, 2000, 30: 110–119.

    Article  Google Scholar 

  13. Kuo, Y. H., Lee, C. S., Chen, C. L., High-stability AWFM filter for signal restoration and its hardware design, Fuzzy Sets and Systems, 2000, 114: 185–202.

    Article  MATH  Google Scholar 

  14. Lee, C. S., Kuo, Y. H., Yu, P. T., Weighted fuzzy mean filters for image processing, Fuzzy Sets and Systems, 1997, 89: 157–180.

    Article  Google Scholar 

  15. Liu Puyin, Representation of digital image by fuzzy neural networks, Fuzzy Sets and Systems, 2002(in press).

  16. Russo, F., Ramponi, G., A fuzzy filter for images corrupted by impulse noise, IEEE Signal Process Lett., 1996, 3: 168–170.

    Article  Google Scholar 

  17. Yang, X., Toh, P. S., Adaptive fuzzy multilevel median filter, IEEE Trans on Image Processing, 1995, 4: 680–682.

    Article  Google Scholar 

  18. Liu Puyin, Li Hongxing, Approximation of generalized fuzzy system to integrable function, Science in China, Series E, 2000, 43(5): 618–628.

    Google Scholar 

  19. Liu Puyin, Li Hongxing, Analyses forL p(μ)-norm approximation capability of generalized Mamdani fuzzy systems, Information Science, 2001, 138: 195–210.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, Beijing Normal University, 100875, Beijing, China

    Liu Puyin & Li Hongxing

Authors
  1. Liu Puyin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Hongxing
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liu Puyin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, P., Li, H. Image restoration techniques based on fuzzy neural networks. Sci China Ser F 45, 273–285 (2002). https://doi.org/10.1360/02yf9024

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1360/02yf9024

Keywords

Search

Navigation