High-Dimensional Space Geometrical Informatics and Its Applications to Image Restoration

  • Shoujue Wang
  • Yu Cao
  • Yi Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


With a view to solve the problems in modern information science, we put forward a new subject named High-Dimensional Space Geometrical Informatics (HDSGI). It builds a bridge between information science and point distribution analysis in high-dimensional space. A good many experimental results certified the correctness and availability of the theory of HDSGI. The proposed method for image restoration is an instance of its application in signal processing. Using an iterative “further blurring-debluring-further blurring” algorithm, the deblured image could be obtained.


Image Restoration Continuous Speech Intrinsic Relation Restoration Result Iterative Deblured Image 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chen, X.: Corpus of Chen Xingshen, p. 244. East China Normal University Press, Shanghai (2002)Google Scholar
  2. 2.
    Wang, S.: Computational Information Geometry and Its Applications. In: Keynote Speech for ICNN&B 2005: Second International Conference on Neural Networks and Brain, vol. 3(1), pp. PL63–PL69 (2005)Google Scholar
  3. 3.
    Wang, S.: Biomimetic Pattern Recognition. Neural Networks Society (INNS, ENNS, JNNS) Newsletter 1(1), 3–5 (2003)Google Scholar
  4. 4.
    Wang, S., Zhao, X.: Biomimetic Pattern Recognition Theory and Its Applications. Chinese Journal of Electronics 13(3) (2004)Google Scholar
  5. 5.
    Wang, S., Xu, J., Wang, X., Qin, H.: Multi-Camera Human-Face Personal Identification System Based on the Biomimetic Pattern Recognition (in Chinese). Acta Electronica Sinica 31(1), 1–4 (2003)Google Scholar
  6. 6.
    Wang, S., Qu, Y., Li, W., Qin, H.: Face Recognition: Biomimetic Pattern Recognition vs. Traditional Pattern Recognition (in Chinese). Acta Electronica Sinica 32(7), 1057–1061 (2004)Google Scholar
  7. 7.
    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
  8. 8.
    Wang, S., Wang, B.: Analysis and Theory of High-Dimension Space Geometry for Artificial Neural Networks (in Chinese). Acta Electronica Sinica 30(1) (2002)Google Scholar
  9. 9.
    Wang, S., Lai, J.: Geometrical Learning, Descriptive Geometry, and Biomimetic Pattern Recognition. Neurocomputing 67(1-4), 9–28 (2005)Google Scholar
  10. 10.
    Kundur, D., Hatzinakos, D.: Blind Image Deconvolution. IEEE Signal Processing Magazine 13(3), 43–64 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shoujue Wang
    • 1
  • Yu Cao
    • 1
  • Yi Huang
    • 1
  1. 1.Laboratory of Artificial Neural Networks, Institute of SemiconductorsChinese Academy of SciencesBeijingChina

Personalised recommendations