  In the last few years, several deep learning models, especially Generative
Adversarial Networks have received a lot of attention for the task of Single
Image Super-Resolution (SISR). These methods focus on building an end-to-end
framework, which produce a high resolution(SR) image from a given low
resolution(LR) image in a single step to achieve state-of-the-art performance.
This paper focuses on improving an existing deep-learning based method to
perform Super-Resolution Microscopy in real-time using a standard GPU. For
this, we first propose a tiling strategy, which takes advantage of parallelism
provided by a GPU to speed up the network training process. Further, we suggest
simple changes to the architecture of the generator and the discriminator of
SRGAN. Subsequently, We compare the quality and the running time for the
outputs produced by our model, opening its applications in different areas like
low-end benchtop and even mobile microscopy. Finally, we explore the
possibility of the trained network to produce High-Resolution HR outputs for
different domains.
