Using an Improved Regularization Method and Rigid Transformation for Super-Resolution Applied to MRI Data

Super-resolution (SR) techniques have shown significant promise in enhancing the resolution of MRI images, which are often limited by hardware constraints and acquisition time. In this study, we introduce an advanced regularization method for MRI super-resolution that integrates spatially adaptive techniques with a robust denoising process to improve image quality. The proposed method excels in preserving high-frequency details while effectively suppressing noise, addressing common limitations of conventional SR approaches. The validation of clinical MRI datasets demonstrates that our approach achieves superior performance compared to traditional algorithms, yielding enhanced image clarity and quantitative improvements in metrics such as the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).