期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 58, 期 -, 页码 130-145出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2014.12.023
关键词
Magnetic resonance imaging; Super-resolution; Sparse representation; Sparse derivative prior; Nonlocal similarity
类别
资金
- China Postdoctoral Science Foundation [2012M511804]
- Natural Science Foundation of Guangdong Medical College [XB1349]
In magnetic resonance (MR) imaging, image spatial resolution is determined by various instrumental limitations and physical considerations. This paper presents a new algorithm for producing a high-resolution version of a low-resolution MR image. The proposed method consists of two consecutive steps: (1) reconstructs a high-resolution MR image from a given low-resolution observation via solving a joint sparse representation and nonlocal similarity L1-norm minimization problem; and (2) applies a sparse derivative prior based post-processing to suppress blurring effects. Extensive experiments on simulated brain MR images and two real clinical MR image datasets validate that the proposed method achieves much better results than many state-of-the-art algorithms in terms of both quantitative measures and visual perception. (C) 2015 Elsevier Ltd. All rights reserved.
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