  Image super-resolution (SR) is one of the vital image processing methods that
improve the resolution of an image in the field of computer vision. In the last
two decades, significant progress has been made in the field of
super-resolution, especially utilizing deep learning methods. This survey is an
effort to provide a detailed survey of recent progress in the field of
super-resolution in the perspective of deep learning while also informing about
the initial classical methods used for achieving super-resolution. The survey
classifies the image SR methods into four categories, i.e., classical methods,
supervised learning-based methods, unsupervised learning-based methods, and
domain-specific SR methods. We also introduce the problem of SR to provide
intuition about image quality metrics, available reference datasets, and SR
challenges. Deep learning-based approaches of SR are evaluated using a
reference dataset. Finally, this survey is concluded with future directions and
trends in the field of SR and open problems in SR to be addressed by the
researchers.
