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Journal Articles Medical Image Analysis Year : 2010

A Non-local Approach for Image Super-Resolution using Intermodality Priors

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, 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.
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Dates and versions

hal-00440313 , version 1 (10-12-2009)
hal-00440313 , version 2 (11-05-2010)

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François Rousseau. A Non-local Approach for Image Super-Resolution using Intermodality Priors. Medical Image Analysis, 2010, 14 (4), pp.594-605. ⟨10.1016/j.media.2010.04.005⟩. ⟨hal-00440313v2⟩

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