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 (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.

DOI: 10.1016/j.media.2010.04.005
01020302011201220132014201520162017
Citations per Year

95 Citations

Semantic Scholar estimates that this publication has 95 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Rousseau2010ANA, title={A non-local approach for image super-resolution using intermodality priors}, author={François Rousseau}, journal={Medical image analysis}, year={2010}, volume={14 4}, pages={594-605} }