Super-resolution (SR) is useful to reduce scan time and/or enhance MR images for better visual perception. High-resolution reference images may improve super-resolution quality, but most previous studies focused on using references from the same subject. Here, we use an image search module to find similar images from other subjects and use transformer based neural networks to learn and transfer the relevant textures to the output. We demonstrate that this approach can outperform single-image super-resolution, and is feasible to achieve high-quality super-resolution at large factors. As the reference images are not limited within a subject, it potentially has wide applications.
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