  With the advent of Deep Learning (DL), Super-Resolution (SR) has also become
a thriving research area. However, despite promising results, the field still
faces challenges that require further research e.g., allowing flexible
upsampling, more effective loss functions, and better evaluation metrics. We
review the domain of SR in light of recent advances, and examine
state-of-the-art models such as diffusion (DDPM) and transformer-based SR
models. We present a critical discussion on contemporary strategies used in SR,
and identify promising yet unexplored research directions. We complement
previous surveys by incorporating the latest developments in the field such as
uncertainty-driven losses, wavelet networks, neural architecture search, novel
normalization methods, and the latests evaluation techniques. We also include
several visualizations for the models and methods throughout each chapter in
order to facilitate a global understanding of the trends in the field. This
review is ultimately aimed at helping researchers to push the boundaries of DL
applied to SR.
