Since magnetic resonance imaging (MRI) can offer images of an object with different contrasts, e.g., T1-weighted or T2-weighted, the shared information between inter-contrast images can be used to benefit super-resolution. Multi-contrast images are assumed to possess the same gradient direction in a local pattern. We proposed to establish a relation model of gradient value between different contrast images, to restore a high-resolution image from its input low-resolution version. The similarity of image patches is employed to estimate intensity parameters, leading a more accurate reconstructed image. Then, iterative back-projection filter is applied to the reconstructed image to further increase image quality. The reconstructed edges are more consistent to the original high-resolution image, indicated with higher PSNR and SSIM than the compared methods.
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