Application of Periodic Frames to Image Restoration

  • Amir Z. Averbuch
  • Pekka Neittaanmaki
  • Valery A. Zheludev
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Abstract

In this chapter, we present examples of image restoration using periodic frames. Images to be restored were degraded by blurring, aggravated by random noise and random loss of significant number of pixels. The images are transformed by periodic frames designed in Sects.  17.2 and  17.4, which are extended to the 2D setting in a standard tensor product way. In the presented experiments, performances of different tight and semi-tight frames are compared between each other in identical conditions.

Keywords

Gaussian Kernel Image Restoration Tight Frame Infinite Impulse Response Fine Texture 
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.
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References

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    T. Goldstein, S. Osher, The split Bregman method for L1-regularized problems. SIAM J. Imaging Sci. 2(2), 323–343 (2009)CrossRefMATHMathSciNetGoogle Scholar
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    H. Ji, Z. Shen, Y. Xu, Wavelet based restoration of images with missing or damaged pixels. East Asian J. Appl. Math. 1(2), 108–131 (2011)MATHGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Amir Z. Averbuch
    • 1
  • Pekka Neittaanmaki
    • 2
  • Valery A. Zheludev
    • 1
  1. 1.School of Computer ScienceTel Aviv UniversityTel AvivIsrael
  2. 2.Mathematical Information TechnologyUniversity of JyväskyläJyväskyläFinland

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