Super Resolution of Medical Images Using Generative Adversarial Networks

This paper aims to present a new model called SRMIGAN that performs super-resolution for MRI and CT medical images to help doctors reach a better diagnosis. This model, SRMIGAN adopts deep learning by applying generative adversarial networks technique. It is developed using MSE loss and by exploiting different optimization techniques. This model is compared to other adopted models by using both objective and subjective metrics. Hence PSNR, SSIM, and mean opinion score results are included. The results show that our model beats the other examined models.