Abstract
Increasing the resolution of the MRI scans results in lowering the signal-to-noise ratio and/or increasing the scan time. In this paper, we improved the resolution of MRI images by obtaining a high resolution image from a sequence of low resolution images using the super-resolution reconstruction methodology. Image restoration is an important step in the reconstruction process, the final appearance and the quality of the reconstructed image depend greatly on the restoration method used. In this paper, we evaluated the performance of three restoration techniques used in the reconstruction, the first one is based on truncated singular value decomposition, the second one is based on Tikhonov regularization and the last one is based on total variation. Experiments are performed on synthetic images and real MRI brain images to verify the results obtained from the numerical simulations.