Medical image resolution enhancement for healthcare using nonlocal self-similarity and low-rank prior

Medical images have high information redundancy, which can be used to improve image analysis and visualization for purpose of healthcare. In order to recover a high-resolution (HR) image from its low-resolution (LR) counterpart, this paper proposes a resolution enhancement method by using the nonlocal self-similar redundancy and the low-rank prior. The proposed method consists of three main steps. First, an initial HR image is generated by nonlocal interpolation, which is based on the self-similarity of medical images. Next, the low-rank minimum variance estimator is exploited to reconstruct the HR image. At last, we iteratively apply the subsampling consistency constraint and perform the low-rank reconstruction to refine the reconstructed HR result. Experimental results conducted on MR and CT images demonstrate that the proposed method outperforms conventional interpolation methods and is competitive with the current stat-of-the-art methods in terms of both quantitative metrics and visual quality.