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

Abstract

Diffusion-weighted (DW) and spectroscopic MR (MRS) 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 sparse representation over a learned overcomplete dictionary based single-image super-resolution (SISR) technique for DW and MRS images. The proposed SISR method incorporates patch-wise sparsity constraint based on external HR information together with the non-local total variation (NLTV) as internal information to make the regularization problem more robust. Experiments are conducted for both DW and MRS test images and results are compared with some of the recent methods. Results indicate the potential of the proposed method for clinical MRI applications.