Improvements to image magnification

  • A. Biancardi
  • L. Lombardi
  • V. Pacaccio
Poster Session A: Color & Texture, Enchancement, Image Analysis & Pattern Recognition, Segmentation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1310)

Abstract

The main limitation of current magnifying techniques is that they do not introduce any new information into the original image. This lack of information is responsible for the perceived degradation of the enlarged image. The idea underlying this work is to estimate missing frequencies from the original low resolution image and to synthesize them. Sub-pixel edge estimation and a polynomial interpolation step are the key techniques of the proposed method. Furthermore, a new extension to color images is presented. Results are encouraging even if they suggest that further effort should be spent in improving edge localization accuracy.

Keywords

Polynomial Curve Enlarge Image Bicubic Interpolation Color Aberration Piecewise Linear Boundary 
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.

References

  1. 1.
    J. Allelbach, P.W. Wong, Edge-Directed Interpolation, Proc. ICIP-96, IEEE Press, Lausanne CH, 1996, vol. III, pp. 707–710.Google Scholar
  2. 2.
    J.D. Fahnestok, B.R. Hunt, The Maintenance of Sharpness In Magnified Digital Images, CVGIP 27, 1984, pp. 32–45.Google Scholar
  3. 3.
    R.G. Keys, Cubic Convolution Interpolation for Digital Image Processing, IEEE Trans. ASSP, vol. 29, no. 6, December 1981, pp. 1153–1160.Google Scholar
  4. 4.
    J.D. Foley, A. van Dam, S.K. Feiner, J.F. Hughes, Computer Graphics, principles and practice, second edition, Addison-Wesley, 1992.Google Scholar
  5. 5.
    A.B. Watson, Perceptual-components architecture for digital video, J. Opt. Soc. Am. A, vol. 7, no. 10, October 1990, pp. 1943–1954.Google Scholar
  6. 6.
    M. Gross, Visual Computing, Springer-Verlag, Berlin, 1994.Google Scholar
  7. 7.
    D.J. Granrath, The role of human visual models in image processing, Proc. of the IEEE, vol 69, no. 5, May 1981, pp. 552–561.Google Scholar
  8. 8.
    D.E. Knuth Digital Halftones by Dot Diffusion, ACM Transactions on Graphics, vol. 6, no. 4, October 1987, pp. 245–273.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • A. Biancardi
    • 1
  • L. Lombardi
    • 1
  • V. Pacaccio
    • 1
  1. 1.Università di Pavia, DISPaviaItaly

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