Super-resolution of Magnetic Resonance Images using deep Convolutional Neural Networks

This research focuses on developing a Super-resolution magnetic resonance (MR) Image restoration method using Convolutional Neural Networks (CNN). The main aim is to train an end to end mapping that takes low-resolution image as input and returns a high-resolution output. Low overhead and a state of the art reconstruction makes the model perform efficiently.