MR image super-resolution via manifold regularized sparse learning

Single image super-resolution (SR) has been shown useful in Magnetic Resonance (MR) image based diagnosis, where the image resolution is still limited. The basic goal of single image SR is to produce a high-resolution (HR) image from corresponding low-resolution (LR) image. However, most existing SR algorithms often fail to: (1) reflect the intrinsic structure between MR images and (2) exploit the intra-patient information of MR images. In fact, MR images are more likely to vary along a low dimensional submanifold, which can be embedded in the high dimensional space. It has also been shown that the structure information of MR images and the priors of the MR images of different modality are important for improving the image resolution. To take full advantage of manifold structure information and intra-patient prior of MR images, a novel single image super-resolution algorithm for MR images is proposed in this paper. Compared with the existing works, the proposed algorithm has the following merits: (1) the proposed sparse coding based algorithm integrates manifold constraints to handle the inverse problem in MR image SR; (2) the manifold structure of the intra-patient MR image is considered for image SR; and (3) the topological structure of the intra-patient MR image can be preserved to improve the reconstructed result. Experiments show that the proposed algorithm is more effective than the state-of-the-art algorithms.