  This study presents a chronological overview of the single image
super-resolution problem. We first define the problem thoroughly and mention
some of the serious challenges. Then the problem formulation and the
performance metrics are defined. We give an overview of the previous methods
relying on reconstruction based solutions and then continue with the deep
learning approaches. We pick 3 landmark architectures and present their results
quantitatively. We see that the latest proposed network gives favorable output
compared to the previous methods.
