  Image super-resolution (SR) is a field in computer vision that focuses on
reconstructing high-resolution images from the respective low-resolution image.
However, super-resolution is a well-known ill-posed problem as most methods
rely on the downsampling method performed on the high-resolution image to form
the low-resolution image to be known. Unfortunately, this is not something that
is available in real-life super-resolution applications such as increasing the
quality of a photo taken on a mobile phone. In this paper we will evaluate
multiple state-of-the-art super-resolution methods and gauge their performance
when presented with various types of real-life images and discuss the benefits
and drawbacks of each method. We also introduce a novel dataset, WideRealSR,
containing real images from a wide variety of sources. Finally, through careful
experimentation and evaluation, we will present a potential solution to
alleviate the generalization problem which is imminent in most state-of-the-art
super-resolution models.
