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Local Geometry Driven Image Magnification and Applications to Super-Resolution

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Advances in Natural Computation (ICNC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4222))

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Abstract

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.

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References

  1. Tan, Y.P., Yap, K.H., Wang, L.P. (eds.): Intelligent Multimedia Processing with Soft Computing. Springer, Heidelberg (2004)

    Google Scholar 

  2. Vernazza, G. (ed.): The IEEE International Conference on Image Processing. Genoa, Italy (2005)

    Google Scholar 

  3. Huang, D.E. (ed.): The International Conference on Intelligent Computing. Springer, Heidelberg (2005)

    Google Scholar 

  4. Campilho, A., Kamel, M.: Image Analysis and Recognition. Springer, Heidelberg (2006)

    Google Scholar 

  5. Blu, T., Thévenaz, P., Unser, M.: Linear Interpolation Revisited. IEEE Transactions on Image Processing 13, 710–719 (2004)

    Article  MathSciNet  Google Scholar 

  6. Li, X., Orchard, T.: New Edge-Directed Interpolation. IEEE Transactions on Image Processing 10, 1521–1527 (2001)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  9. Guichard, F., Malgouyres, F.: Total Variation based Interpolation. EUSIPSO III, 1741–1744 (1998)

    Google Scholar 

  10. Chan, T.F., Shen, J.H.: Mathematical Models for Local Nontexture Inpaintings. SIAM J. Appl. Math. 62(3), 1019–1043 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  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. Morse, B.S., Schwartzwald, D.: Isophote-based Interpolation. In: 5th IEEE International Conference on Image Processing (1998)

    Google Scholar 

  13. Osher, S.J., Rudin, L.I.: Feature-Oriented Image Enhancement Using Shock Filters. SIAM J. Numer. Anal. 27, 919–940 (1990)

    Article  MATH  Google Scholar 

  14. Alvarez, L., Mazorra, L.: Signal and Image Restoration Using Shock Filters and Anisotropic Diffusion. SIAM J. Numer. Anal. 31(2), 590–605 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  15. Weickert, J.: Coherence-Enhancing Diffusion Filtering. International Journal of Computer Vision 31(2/3), 111–127 (1999)

    Article  Google Scholar 

  16. Weickert, J.: Coherence-Enhancing Shock Filters. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 1–8. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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. Farsiu, S., Robinson, M.D.: Fast and Robust Multiframe Super Resolution. IEEE Transactions on Image Processing 13(10), 1327–1344 (2004)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  21. Nguen, M.K., Bose, N.K.: Mathematical Analysis of Superresolution Methodology. IEEE Signal Processing Magazine, 62–74 (2003)

    Google Scholar 

  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. Zomet, A., Peleg, S.: Efficient Super-resolution and Applications to Mosaics. In: Proc. Int. Conf. Pattern Recognition, pp. 579–583 (2003)

    Google Scholar 

  24. Banham, M.R., Katsaggelos, A.K.: Digital Image Restoration. IEEE Trans. Signal Processing, 24–41 (1997)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China

    Wenze Shao

  2. Graduate School, Nanjing University of Science and Technology, 210094, Nanjing, China

    Zhihui Wei

Authors
  1. Wenze Shao
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  2. Zhihui Wei
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Editor information

Editors and Affiliations

  1. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  3. School of Electronic Engineering, Xidian Univ., P.O. Box, 710071, Xi’an, P.R. China

    Xinbo Gao

  4. College of Mathematics and Information Science, Hebei Normal University, 050016, Shijiazhuang, Hebei, P.R. China

    Jing Liu

  5. Multi-Agent Systems Lab,Department of Computer Science, University of Science and Technology of China, 230026, Hefei, China

    Feng Wu

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© 2006 Springer-Verlag Berlin Heidelberg

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Shao, W., Wei, Z. (2006). Local Geometry Driven Image Magnification and Applications to Super-Resolution. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_93

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  • DOI: https://doi.org/10.1007/11881223_93

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45907-1

  • Online ISBN: 978-3-540-45909-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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