Advertisement

An Automatic Image Scaling Up Algorithm

  • Maria Frucci
  • Carlo Arcelli
  • Gabriella Sanniti di Baja
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7329)

Abstract

A fully automatic scaling up algorithm is presented in the framework of interpolation methods. For any integer zooming factor n, the algorithm generates a magnified version of an input color image in one scan of the image. The computational complexity of the algorithm is O(N), where N is the size of the input image. The visual aspect of the magnified images is generally appealing also when considering large zooming factors. Peak Signal to Noise Ratio and Structural SIMilarity are used to evaluate the performance of the algorithm and to compare it with other scaling up algorithms.

Keywords

digital images color images zooming interpolation 

References

  1. 1.
    Lehmann, T.M., Gonner, C., Spitzer, K.: Survey: interpolation methods in medical image processing. IEEE Trans. Medical Imaging 18, 1049–1075 (1999)CrossRefGoogle Scholar
  2. 2.
    Amanatiadis, A., Andreadis, I.: A survey on evaluation methods for image interpolation. Measurement Science and Technology 20(10), 104015–104023 (2009)CrossRefGoogle Scholar
  3. 3.
    Li, X., Orchard, M.T.: New edge-directed interpolation. IEEE Trans. Image Processing 10(10), 1521–1527 (2001)CrossRefGoogle Scholar
  4. 4.
    Zhang, L., Wu, X.: An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans. Image Processing 15(8), 2226–2238 (2006)CrossRefGoogle Scholar
  5. 5.
    Takeda, H., Farsiu, S., Milanfar, P.: Kernel regression for image processing and reconstruction. IEEE Trans. Image Processing 16(2), 349–366 (2007)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Zhang, X., Wu, X.: Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation. IEEE Trans. Image Processing 17(6), 887–896 (2008)CrossRefGoogle Scholar
  7. 7.
    Mallat, S., Yu, G.: Super-resolution with sparse mixing estimators. IEEE Trans. Image Processing 19(11), 2889–2900 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Jurio, A., Pagola, M., Mesiar, R., Beliakov, G., Bustince, H.: Image magnification using interval information. IEEE Trans. Image Processing 20(11), 3112–3123 (2011)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Schultz, R.R., Stevenson, R.L.: A Bayesian approach to image expansion for improved definition. IEEE Trans. Image Processing 3(3), 233–242 (1994)CrossRefGoogle Scholar
  10. 10.
    Carey, W.K., Chuang, D.B., Hemami, S.S.: Regularity preserving image Interpolation. IEEE Trans. Image Processing 8(9), 1293–1297 (1999)CrossRefGoogle Scholar
  11. 11.
    Mitra, S.K., Murthy, C.A., Kundu, M.K.: A technique for image magnification using partitioned iterative function system. Pattern Recognition 33(7), 1119–1133 (2000)CrossRefGoogle Scholar
  12. 12.
    Tschumperle, D., Deriche, R.: Vector-valued image regularization with PDEs: a common framework for different applications. IEEE Trans. Pattern Analysis and Machine Intelligence 27(4), 506–517 (2005)CrossRefGoogle Scholar
  13. 13.
    Arcelli, C., Brancati, N., Frucci, M., Ramella, G., Sanniti di Baja, G.: A fully automatic one-scan adaptive zooming algorithm for color images. Signal Processing 91, 61–71 (2010)CrossRefGoogle Scholar
  14. 14.
    De Simone, F., Ticca, D., Dufaux, F., Ansorge, M., Ebrahimi, T.: A comparative study of color image compression standards using perceptually driven quality metrics. In: Proc. SPIE Conf. Optics and Photonics, Applications of Digital Image Processing XXXI (2008)Google Scholar
  15. 15.
    Wang, Z., Lu, L., Bovik, A.C.: Video quality assessment based on structural distortion measurement. Signal Processing: Image Communication 19(2), 121–132 (2004)CrossRefGoogle Scholar
  16. 16.
  17. 17.
  18. 18.
  19. 19.
    Asuni, N., Giachetti, A.: Accuracy Improvements and artifacts removal in edge based image interpolation. In: Proc. VISAPP 2008, pp. 58–65 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Maria Frucci
    • 1
  • Carlo Arcelli
    • 1
  • Gabriella Sanniti di Baja
    • 1
  1. 1.Istituto di Cibernetica “E.Caianiello”, CNRPozzuoliItaly

Personalised recommendations