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Edge-Directed Image Interpolation Using Color Gradient Information

  • Andrey Krylov
  • Andrey Nasonov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6979)

Abstract

Image resampling method using color edge-directed interpolation has been developed. It uses color image gradient to perform the interpolation across image gradient rather than along image gradient. The developed combined method takes color low resolution image and grayscale high resolution image obtained by a non-linear image resampling method as an input. It includes consecutive calculation stages for high resolution color gradient, for high resolution color information interpolation and finally for high resolution color image assembling.

The concept of color basic edges is used to analyze the results of color image resampling. Color basic edge points metric was suggested and used to show the effectiveness of the proposed image interpolation method.

Keywords

color image upsampling gradient based interpolation 

References

  1. 1.
    Blu, T., Thevenaz, P., Unser, M.: Linear interpolation revitalized. IEEE Trans. Image Proc. 13(5), 710–719 (2004)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Chena, M.J., Huanga, C.H., Leea, W.L.: A fast edge-oriented algorithm for image interpolation. Image and Vision Computing 23(9), 791–798 (2005)CrossRefGoogle Scholar
  3. 3.
    Di Zenzo, S.: A note on the gradient of a multi-image. Comput. Vision Graph. Image Process. 33, 116–125 (1986)CrossRefzbMATHGoogle Scholar
  4. 4.
    Koschan, A.: A comparative study on color edge detection. In: Li, S., Teoh, E.-K., Mital, D., Wang, H. (eds.) ACCV 1995. LNCS, vol. 1035, pp. 574–578. Springer, Heidelberg (1996)Google Scholar
  5. 5.
    Krylov, A.S., Lukin, A.S., Nasonov, A.V.: Edge-preserving nonlinear iterative image resampling method. In: Proceedings of International Conference on Image Processing (ICIP 2009), pp. 385–388 (2009)Google Scholar
  6. 6.
    Lee, Y.J., Yoon, J.: Nonlinear image upsampling method based on radial basis function interpolation. IEEE Trans. Image Proc. 19(10), 2682–2692 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Leitao, J.A., Zhao, M., de Haan, G.: Content-adaptive video up-scaling for high-definition displays. In: Proceedings of Image and Video Communications and Processing 2003, vol. 5022, pp. 612–622 (2003)Google Scholar
  8. 8.
    Li, X., Gunturk, B., Zhang, L.: Image demosaicing: a systematic survey. Visual Communications and Image Processing 6822, 68221J–68221J–15 (2008)Google Scholar
  9. 9.
    Nasonov, A.V., Krylov, A.S.: Basic edges metrics for image deblurring. In: Proceedings of 10th Conference on Pattern Recognition and Image Analysis: New Information Technologies, vol. 1, pp. 243–246 (2010)Google Scholar
  10. 10.
    Sheikh, H., Sabir, M., Bovik, A.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Proc. 15(11), 3440–3451 (2006)CrossRefGoogle Scholar
  11. 11.
    Sheikh, H., Wang, Z., Cormack, L., Bovik, A.: Live image quality assessment database release 2, http://live.ece.utexas.edu/research/quality
  12. 12.
    Sun, J., Xu, Z., Shum, H.Y.: Image super-resolution using gradient profile prior. In: IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2008), pp. 1–8 (2008)Google Scholar
  13. 13.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Proc. 13(4), 600–612 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Andrey Krylov
    • 1
  • Andrey Nasonov
    • 1
  1. 1.Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and CyberneticsLomonosov Moscow State UniversityLeninskie goryRussia

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