Local Geometry Driven Image Magnification and Applications to Super-Resolution

  • Wenze Shao
  • Zhihui Wei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


Though there have been proposed many magnification works in literatures, magnification in this paper is approached as reconstructing the geometric structures of the original high-resolution image. The structure tensor is able to estimate the orientation of both the edges and flow-like textures, which hence is much appropriate to magnification. Firstly, an edge-enhancing PDE and a corner-growing PDE are respectively proposed based on the structure tensor. Then, the two PDE’s are combined into a novel one, which not only enables to enhance the edges and flow-like textures, but also to preserve the corner structures. Finally, the novel PDE is applied to image magnification. The method is simple, fast and robust to both the noise and the blocking-artifact. Another novelty in the paper is the application of the novel PDE to super-resolution reconstruction, plus additional term for image fidelity. Experiment results demonstrate the effectiveness of our approach.


Structure Tensor Image Magnification Bicubic Interpolation Local Coherence IEEE Signal Processing Magazine 
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  1. 1.
    Tan, Y.P., Yap, K.H., Wang, L.P. (eds.): Intelligent Multimedia Processing with Soft Computing. Springer, Heidelberg (2004)Google Scholar
  2. 2.
    Vernazza, G. (ed.): The IEEE International Conference on Image Processing. Genoa, Italy (2005)Google Scholar
  3. 3.
    Huang, D.E. (ed.): The International Conference on Intelligent Computing. Springer, Heidelberg (2005)Google Scholar
  4. 4.
    Campilho, A., Kamel, M.: Image Analysis and Recognition. Springer, Heidelberg (2006)Google Scholar
  5. 5.
    Blu, T., Thévenaz, P., Unser, M.: Linear Interpolation Revisited. IEEE Transactions on Image Processing 13, 710–719 (2004)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Li, X., Orchard, T.: New Edge-Directed Interpolation. IEEE Transactions on Image Processing 10, 1521–1527 (2001)CrossRefGoogle Scholar
  7. 7.
    El-Khamy, S.E., Hadhoud, M.M., Dessouky, M.I., Salam, B.M., El-Samie, F.E.: Efficient Implementation of Image Interpolation as an Inverse Problem. Digital Signal Processing 15, 137–152 (2005)CrossRefGoogle Scholar
  8. 8.
    Schultz, R.R., Stevenson, R.L.: A Bayesian Approach to Image Expansion for Improved Definition. IEEE Transactions on Image Processing 3, 233–242 (1994)CrossRefGoogle Scholar
  9. 9.
    Guichard, F., Malgouyres, F.: Total Variation based Interpolation. EUSIPSO III, 1741–1744 (1998)Google Scholar
  10. 10.
    Chan, T.F., Shen, J.H.: Mathematical Models for Local Nontexture Inpaintings. SIAM J. Appl. Math. 62(3), 1019–1043 (2002)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Belahmidi, A., Guichard, F.: A Partial Differential Equation Approach to Image Zoom. In: Proceedings of International Conference on Image Processing (2004)Google Scholar
  12. 12.
    Morse, B.S., Schwartzwald, D.: Isophote-based Interpolation. In: 5th IEEE International Conference on Image Processing (1998)Google Scholar
  13. 13.
    Osher, S.J., Rudin, L.I.: Feature-Oriented Image Enhancement Using Shock Filters. SIAM J. Numer. Anal. 27, 919–940 (1990)MATHCrossRefGoogle Scholar
  14. 14.
    Alvarez, L., Mazorra, L.: Signal and Image Restoration Using Shock Filters and Anisotropic Diffusion. SIAM J. Numer. Anal. 31(2), 590–605 (1994)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Weickert, J.: Coherence-Enhancing Diffusion Filtering. International Journal of Computer Vision 31(2/3), 111–127 (1999)CrossRefGoogle Scholar
  16. 16.
    Weickert, J.: Coherence-Enhancing Shock Filters. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 1–8. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  17. 17.
    Weickert, J.: A Scheme for Coherence-Enhancing Diffusion Filtering with Optimized Rotation Invariance. Journal of Visual Communication and Image Representation 13(1/2), 103–118 (2002)CrossRefGoogle Scholar
  18. 18.
    Borman, S., Stevenson, R.L.: Super-resolution for Image Sequences—A Review. In: Proc. IEEE Int. Symp. Circuits and Systems, pp. 374–378 (1998)Google Scholar
  19. 19.
    Farsiu, S., Robinson, M.D.: Fast and Robust Multiframe Super Resolution. IEEE Transactions on Image Processing 13(10), 1327–1344 (2004)CrossRefGoogle Scholar
  20. 20.
    Park, S.C., Park, M.K., Kang, M.G.: Super-resolution Image Reconstruction – A Technical Overview. IEEE Signal Processing Magazine 20(3), 21–36 (2003)CrossRefGoogle Scholar
  21. 21.
    Nguen, M.K., Bose, N.K.: Mathematical Analysis of Superresolution Methodology. IEEE Signal Processing Magazine, 62–74 (2003)Google Scholar
  22. 22.
    Capel, D., Zisserman, A.: Super-resolution Enhancement of Text Image Sequences. In: Proceedings of International Conference on Pattern Recognition, pp. 600–605 (2000)Google Scholar
  23. 23.
    Zomet, A., Peleg, S.: Efficient Super-resolution and Applications to Mosaics. In: Proc. Int. Conf. Pattern Recognition, pp. 579–583 (2003)Google Scholar
  24. 24.
    Banham, M.R., Katsaggelos, A.K.: Digital Image Restoration. IEEE Trans. Signal Processing, 24–41 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wenze Shao
    • 1
  • Zhihui Wei
    • 2
  1. 1.Department of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.Graduate SchoolNanjing University of Science and TechnologyNanjingChina

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