Evaluation of Classic Super-Resolution Algorithms for Magnetic Resonance Images

Magnetic Resonance Imaging (MRI) is an important area in medical enviroment. Many studies base their diagnosis upon what it seems in the images. However, there are critical issues while dealing with MRI; noise sensibility, long acquisition sessions, appearance of artifacts, blurring, etc. One way to counter measure blurring and long sessions is by using Super-Resolution (SR) algorithms. In this work, we present three approachs to evaluate the selected SR algorithms in MRI: 1) Enhance images' resolution from whole studies with three classical SR algorithms, 2) use quantitative metrics to evaluate the chosen algorithms, 3) do a measure of processing time through computer's CPU. Our results determined which algorithm has better performance at processing MRI in terms of image quality and processing time.