  In recent years, there have been several advancements in the task of image
super-resolution using the state of the art Deep Learning-based architectures.
Many super-resolution-based techniques previously published, require high-end
and top-of-the-line Graphics Processing Unit (GPUs) to perform image
super-resolution. With the increasing advancements in Deep Learning approaches,
neural networks have become more and more compute hungry. We took a step back
and, focused on creating a real-time efficient solution. We present an
architecture that is faster and smaller in terms of its memory footprint. The
proposed architecture uses Depth-wise Separable Convolutions to extract
features and, it performs on-par with other super-resolution GANs (Generative
Adversarial Networks) while maintaining real-time inference and a low memory
footprint. A real-time super-resolution enables streaming high resolution media
content even under poor bandwidth conditions. While maintaining an efficient
trade-off between the accuracy and latency, we are able to produce a comparable
performance model which is one-eighth (1/8) the size of super-resolution GANs
and computes 74 times faster than super-resolution GANs.

Image and Video Processing;Computer Vision and Pattern Recognition