Solving the inverse problem of image zooming using "self-examples"

Authors:
Mehran Ebrahimi;Edward R. Vrscay
Affiliations:
Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada;Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada
Venue:
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Year:
2007

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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.