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Recently, the Magnetic Resonance Imaging (MRI) images have limited andunsatisfactory resolutions due to various constraints such as physical,technological and economic considerations. Super-resolution techniques canobtain high-resolution MRI images. The traditional methods obtained theresolution enhancement of brain MRI by interpolations, affecting the accuracyof the following diagnose process. The requirement for brain image quality isfast increasing. In this paper, we propose an image super-resolution (SR)method based on overcomplete dictionaries and inherent similarity of an imageto recover the high-resolution (HR) image from a single low-resolution (LR)image. We explore the nonlocal similarity of the image to tentatively searchfor similar blocks in the whole image and present a joint reconstruction methodbased on compressive sensing (CS) and similarity constraints. The sparsity andself-similarity of the image blocks are taken as the constraints. The proposedmethod is summarized in the following steps. First, a dictionary classificationmethod based on the measurement domain is presented. The image blocks areclassified into smooth, texture and edge parts by analyzing their features inthe measurement domain. Then, the corresponding dictionaries are trained usingthe classified image blocks. Equally important, in the reconstruction part, weuse the CS reconstruction method to recover the HR brain MRI image, consideringboth nonlocal similarity and the sparsity of an image as the constraints. Thismethod performs better both visually and quantitatively than some existingmethods.

Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity