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

François Rousseau (1)

1 LSIIT - Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection

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.

Mots clés: inverse problem; super-resolution; non-local approach

Domaines: Imagerie médicale; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

François Rousseau. A Non-local Approach for Image Super-Resolution using Intermodality Priors. 2009. ⟨hal-00440313v1⟩