  Super-resolution (SR), the process of obtaining high-resolution images from
one or more low-resolution observations of the same scene, has been a very
popular topic of research in the last few decades in both signal processing and
image processing areas. Due to the recent developments in Convolutional Neural
Networks, the popularity of SR algorithms has skyrocketed as the barrier of
entry has been lowered significantly. Recently, this popularity has spread into
video processing areas to the lengths of developing SR models that work in
real-time. In this paper, we compare different SR models that specialize in
single image processing and will take a glance at how they evolved to take on
many different objectives and shapes over the years.
