Title: Brain MR image super-resolution reconstruction via sparse representation and non-local similarity regularisation

Authors: Di Zhang; Jiazhong He; Yun Zhao

Addresses: School of Information Engineering, Guangdong Medical College, Dongguan, Guangdong, China ' Department of Physics, Shaoguan University, Shaoguan, Guangdong, China ' School of Information Engineering, Guangdong Medical College, Dongguan, Guangdong, China

Abstract: In magnetic resonance imaging, image resolution is limited by several factors such as hardware constraints or physical considerations. In many cases, the acquired images have to be magnified to match a specific resolution. This paper presents a new super-resolution reconstruction algorithm to generate a high-resolution version of a low-resolution brain MR image. The proposed approach uses a multi-scale first- and second-order derivative analysis to estimate the missing high-frequency information and integrates sparse representation and non-local similarity regularisation into a unified L1-norm minimisation framework. Extensive experiments on brain MR image super-resolution 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.

Keywords: magnetic resonance imaging; super-resolution image reconstruction; sparse representation; non-local similarity; brain MRI; brain images; image resolution.

DOI: 10.1504/IJBET.2014.062741

International Journal of Biomedical Engineering and Technology, 2014 Vol.15 No.2, pp.95 - 113

Received: 12 Mar 2014
Accepted: 04 May 2014
Published online: 21 Oct 2014 *

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