  Spatial resolution of medical images can be improved using super-resolution
methods. Real Enhanced Super Resolution Generative Adversarial Network
(Real-ESRGAN) is one of the recent effective approaches utilized to produce
higher resolution images, given input images of lower resolution. In this
paper, we apply this method to enhance the spatial resolution of 2D MR images.
In our proposed approach, we slightly modify the structure of the Real-ESRGAN
to train 2D Magnetic Resonance images (MRI) taken from the Brain Tumor
Segmentation Challenge (BraTS) 2018 dataset. The obtained results are validated
qualitatively and quantitatively by computing SSIM (Structural Similarity Index
Measure), NRMSE (Normalized Root Mean Square Error), MAE (Mean Absolute Error),
and VIF (Visual Information Fidelity) values.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning