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A Novel Image Restoration Algorithm Based on High-Dimensional Space Geometry

  • Wenming Cao
  • Mei-fen Xie
  • Shoujue Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3984)

Abstract

A novel image restoration approach based on high-dimensional space geometry is proposed, which is quite different from the existing traditional image restoration techniques. It is based on the homeomorphisms and “Principle of Homology Continuity” (PHC), an image is mapped to a point in high-dimensional space. Begin with the original blurred image, we get two further blurred images, then the restored image can be obtained through the regressive curve derived from the three points which are mapped form the images. Experiments have proved the availability of this “blurred-blurred-restored” algorithm, and the comparison with the classical Wiener Filter approach is presented in final.

Keywords

Image Restoration Degraded Image Deblurred Image IEEE Signal Processing Magazine Neural Network Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Banham, M., Katsaggelos, A.: Digital image restoration. IEEE Signal Processing Magazine 24241, 24–38 (1997)CrossRefGoogle Scholar
  2. 2.
    Rafael, C.G., Richard, E.W.: Digital Image Processing, 2nd edn., vol. 7, pp. 175–216. Publishing House of Electronics, Beijing (2002)Google Scholar
  3. 3.
    Jixiang, S.: Image Processing in Chinese, vol. 9, pp. 201–250. Publishing House of Science, Beijing (2004)Google Scholar
  4. 4.
    Zhou, Y.-T., Chellappa, R., Vaid, A., Jenkins, B.K.: Image restoration using a neural network. IEEE Trans. Acoust. Speech. Signal Processing 36, 1141–1151 (1998)CrossRefGoogle Scholar
  5. 5.
    Paik, J.K., Katsaggelos, A.K.: Image restoration using a modified Hopfield network. IEEE Trans. Image Processing 1, 49–63 (1992)CrossRefGoogle Scholar
  6. 6.
    Wang, S.-j.: Biomimetic(Topological) Pattern Recognition DDA New Model of Pattern Recognition Theory and Its applications. Chinese Journal of Electronics 30, 1417–1420 (2002)Google Scholar
  7. 7.
    Wang, S.-j., Xu, J., Wang, X.-B., Qin, H.: Multi-Camera Human-Face Personal Identification System based on the BIOMIETIC PATTERN TRCOGNITION. Chinese Journal of Electronics 31, 1–4 (2003)Google Scholar
  8. 8.
    Cao, W., Pan, X., Wang, S.: Continuous speech research based on two-weight neural network. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 345–350. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Wang, S.: Computational Information Geometry and its Applications. Keynote Speech for the second International Conference on Neural Networks and Brain. In: Proceedings of 2005 internal conference on neural netwroks and brains, Beijing, China, vol. 1, pp. 63–69 (2005)Google Scholar
  10. 10.
    Wang, S., Yu, C., Yi, H.: A Novel Image Restoration Approach Based on Point Location in High-dimensional Space Geometry. In: Proceedings of 2005 internal conference on neural netwroks and brains, Beijing, China, vol. 3, pp. 301–305 (2005)Google Scholar
  11. 11.
    Zhengshuai, L., Ying, H., Zhenzhong, R.: Analysissitus Fundamention (in Chinese), pp. 47–51. Publishing House of HeNan University, KaiFeng (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wenming Cao
    • 1
    • 2
  • Mei-fen Xie
    • 1
  • Shoujue Wang
    • 2
  1. 1.Institution of Intelligent Information system, College of Information of EngineeringZhejiang University of TechnologyHangzhouChina
  2. 2.Institute of semiconductors of Chinese Academy of ScienceBeijingChina

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