MR image super-resolution reconstruction using sparse representation, nonlocal similarity and sparse derivative prior

作者：Zhang, Di^*; He, Jiazhong; Zhao, Yun; Du, Minghui

来源：Computers in Biology and Medicine, 2015, 58: 130-145.

DOI：10.1016/j.compbiomed.2014.12.023

• Magnetic resonance imaging
• Super-resolution
• Sparse representation
• Sparse derivative prior
• Nonlocal similarity

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.

• 出版日期2015-3-1
• 单位华南理工大学; 广东医科大学; 韶关学院