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. Regarding the image as a locally stationary Gaussian process and using the least square method, we found weights of a local window are to be nearly invariant to image contrasts, which can be further used to transfer the shared information from one contrast to another. We analyze this property with comprehensive mathematics and numeric experiments. 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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