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Signal Reconstruction by Projection Filter with Preservation of Preferential Components

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4253))

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Abstract

Signal reconstruction is one of the knowledge extration problems. Especially in this problem, knowledge means underlying signal or image, which is extracted from the downsampled one. In this paper, we propose a novel reconstruction filter which perfectly reconstructs predetermined preferential components, and makes a reconstructed sigal/image agree with the oblique projection of an original one. It enables us to get rid of artifacts which arise in reconstructed signals by the conventional partial projection filter when the number of samples is small compared with the dimension of the approximation subspace. By simulations, we show that the proposed filter performs better than the conventional methods.

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References

  1. Andrew, H.C., Hunt, R.B.: Digital Image Restoration. Prentice Hall, New York (1977)

    Google Scholar 

  2. Ogawa, H., Nakamura, N.: Projection filter restoration of degraded images. In: Proc. Int. Conference on Pattern Recognition 1984, Montreal Canada, July 30-August 2, pp. 601–603 (1984)

    Google Scholar 

  3. Nakamura, N., Ogawa, H.: Optimum digital image restoration under additive noises. The Transactions of IEICE D J67-D, 563–570 (1984) (in Japanese); Its English translation appeared in Systems·Computers·Controls, Scripta Technica, Inc., USA, vol. 15, pp. 73–82 (1984)

    Google Scholar 

  4. Ogawa, H., Hara, S.: Partial projection filter for image restoration. In: Proc. 6th Int. Scandinavian Conference on Image Analysis, Oulu, Finland, June 19-22, 1989, pp. 270–277 (1989)

    Google Scholar 

  5. Albert, A.: Regression and the Moore-Penrose Pseudoinverse. Academic Press, New York (1972)

    MATH  Google Scholar 

  6. Eldar, Y.C.: Sampling and reconstruction in arbitrary spaces and oblique dual frame vectors. The Journal of Fourier Analysis and Applications 1(9), 77–96 (2003)

    MathSciNet  Google Scholar 

  7. Hirabayashi, A., Unser, M.: An extension of oblique projection sampling theorem. In: Proc. the 2005 International Conference on Sampling Theory and Applications (SampTA 2005), Samsun, Turkey, July 10–14, 2005, pp. 1–6 (2005) (CD-ROM)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Yamaguchi University, Ube Yamaguchi, 755-8611, Japan

    Akira Hirabayashi

  2. Omron Corporation, Social Systems Solutions Business Company, 2-2-1, Nishikusatsu, 525-0035, Kusatsu, Japan

    Takeshi Naito

Authors
  1. Akira Hirabayashi
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  2. Takeshi Naito
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Editor information

Editors and Affiliations

  1. School of Design, Engineering and Computing, Bournemouth University, UK

    Bogdan Gabrys

  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Lakhmi C. Jain

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© 2006 Springer-Verlag Berlin Heidelberg

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Hirabayashi, A., Naito, T. (2006). Signal Reconstruction by Projection Filter with Preservation of Preferential Components. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_161

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  • DOI: https://doi.org/10.1007/11893011_161

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46542-3

  • Online ISBN: 978-3-540-46544-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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