Sparse Representation Based Super-Resolution of MRI Images with Non-Local Total Variation Regularization

Abstract

Diffusion-weighted and Spectroscopic MR images are found to be very helpful for diagnostic purposes as they provide complementary information to that provided by conventional MRI. These images are also acquired at a faster rate, but with low signal-to-noise ratio. This limitation can be overcome by applying image super-resolution techniques. In this paper, we propose a single-image super-resolution (SISR) technique via sparse representation for diffusion-weighted (DW) and spectroscopic MR (MRS) images. It is based on non-local total variation approach to regularize an ill-posed inverse problem of SISR. Experiments are conducted for both DW and MRS test images and the results are compared with other recent regularization-based methods using sparse representation. The comparison also validates the potential of the proposed method for clinical applications.