A Transform-Domain Approach with Symmetric and Edge Constraints for MRI Super-Resolution

Magnetic resonance imaging (MRI) provides highquality soft tissue contrast images and is crucial in medical diagnosis. However, systems face trade-offs between image resolution and scan time. Low-resolution MRI scans reduce scan time and patient burden but lose critical details needed for accurate diagnosis. To address this problem, super-resolution techniques have been developed to improve the clarity of lowresolution input images. Single-image super-resolution (SISR), which minimizes patient scanning time, has gradually become a research focus, but existing methods often struggle to balance the reconstruction of low-frequency structural information and high-frequency details. In this paper, we propose a novel superresolution up-sampling pipeline that enhances both the highfrequency and low-frequency components of magnetic resonance imaging. In addition, we introduce an enhanced loss function that includes symmetry and edge constraints to preserve critical structural details for improved diagnostic accuracy. The extensive experiments across multiple datasets validate the effectiveness of our SISR model. Source code will be made publicly available.