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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Ebrahimi, M., & Vrscay, E. R. (2007). Solving the inverse problem of image zooming using “self- examples.” In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4633 LNCS, pp. 117–130). Springer Verlag. https://doi.org/10.1007/978-3-540-74260-9_11
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