Solving the Inverse Problem of Image Zooming Using “Self-Examples”

  • Mehran Ebrahimi
  • Edward R. Vrscay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4633)

Abstract

In this paper we present a novel single-frame image zooming technique based on so-called “self-examples”. Our method combines the ideas of fractal-based image zooming, example-based zooming, and nonlocal-means image denoising in a consistent and improved framework. In Bayesian terms, this example-based zooming technique targets the MMSE estimate by learning the posterior directly from examples taken from the image itself at a different scale, similar to fractal-based techniques. The examples are weighted according to a scheme introduced by Buades et al. to perform nonlocal-means image denoising. Finally, various computational issues are addressed and some results of this image zooming method applied to natural images are presented.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Mehran Ebrahimi
    • 1
  • Edward R. Vrscay
    • 1
  1. 1.Department of Applied Mathematics, Faculty of Mathematics, University of Waterloo, Waterloo, Ontario, N2L 3G1Canada

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