Solving the Inverse Problem of Image Zooming Using "Self-Examples"

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.

• Type: Conference
• Year: 2007
• Where: ICIAR
• Authors: Mehran Ebrahimi, Edward R. Vrscay
• Keywords: ICIAR 2007, Image Denoising, Image Zooming Technique, Images