Brain MR image super-resolution reconstruction via sparse representation and non-local similarity regularisation
by Di Zhang; Jiazhong He; Yun Zhao
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 15, No. 2, 2014

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.

Online publication date: Tue, 21-Oct-2014

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