title: MR image super-resolution reconstruction using sparse representation, nonlocal similarity and sparse derivative prior.
abstract: 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.
authors: School of Information Engineering, Guangdong Medical College, Dongguan, China; School of Electronics and Information, South China University of Technology, Guangzhou, China. Electronic address: haihaiwenqi@163.com.
authors: Di
authors: D
authors: Zhang
authors: Department of Physics, Shaoguan University, Shaoguan, China.
authors: Jiazhong
authors: J
authors: He
authors: School of Information Engineering, Guangdong Medical College, Dongguan, China.
authors: Yun
authors: Y
authors: Zhao
authors: School of Electronics and Information, South China University of Technology, Guangzhou, China.
authors: Minghui
authors: M
authors: Du
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000473:pathology; D016208:Databases, Factual; D006321:Heart / Q000033:anatomy & histology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009206:Myocardium / Q000473:pathology; D059629:Signal-To-Noise Ratio
keywords: Magnetic resonance imaging; Nonlocal similarity; Sparse derivative prior; Sparse representation; Super-resolution