Improving x-ray image resolution using subpixel shifts of the detector

The resolution of digitized images is linked to the detector array pixel size. Aliasing effects result from a non- adequation between the detector sampling and the signal bandwidths. The aim of this study is to develop a super- resolution algorithm for X-ray images. Our technique uses controlled horizontal and vertical subpixel shifts. Generalized sampling theorem of Papoulis, based on a multichannel approach, is the theoretical justification for the recovery of a high resolution image thanks to a set of low resolution ones. A higher resolution image is recovered by a minimization of a quadratic criterion. An iterative relaxation method is used to compute the minimum. To regularize, a priori data about the signal are introduced in order to fight against noise effects. Because of the opposite effects of regularization and super-resolution an adapted regularization that preserves discontinuities has to be used. Results obtained show that our algorithm recovers high frequency components on X-ray images without noise amplification. An analysis of real acquisitions in terms of modulation transfer function (MTF) shows that we obtain, thanks to this method, a 'virtual' detector better than a low resolution one, and equivalent to a real high resolution one.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.