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Med Image Anal. 2010 Aug;14(4):594-605. doi: 10.1016/j.media.2010.04.005. Epub 2010 May 6.

A non-local approach for image super-resolution using intermodality priors.

Author information

  • 1LSIIT, UMR 7005 CNRS-Université de Strasbourg, 67412 Illkirch, France. rousseau@unistra.fr

Abstract

Image enhancement is of great importance in medical imaging where image resolution remains a crucial point in many image analysis algorithms. In this paper, we investigate brain hallucination (Rousseau, 2008), or generating a high-resolution brain image from an input low-resolution image, with the help of another high-resolution brain image. We propose an approach for image super-resolution by using anatomical intermodality priors from a reference image. Contrary to interpolation techniques, in order to be able to recover fine details in images, the reconstruction process is based on a physical model of image acquisition. Another contribution to this inverse problem is a new regularization approach that uses an example-based framework integrating non-local similarity constraints to handle in a better way repetitive structures and texture. The effectiveness of our approach is demonstrated by experiments on realistic Brainweb Magnetic Resonance images and on clinical images from ADNI, generating automatically high-quality brain images from low-resolution input.

Copyright 2010 Elsevier B.V. All rights reserved.

PMID:
20580893
[PubMed - indexed for MEDLINE]
PMCID:
PMC2947386
Free PMC Article

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