  Single-Image Super-Resolution can support robotic tasks in environments where
a reliable visual stream is required to monitor the mission, handle
teleoperation or study relevant visual details. In this work, we propose an
efficient Generative Adversarial Network model for real-time Super-Resolution,
called EdgeSRGAN (code available at https://github.com/PIC4SeR/EdgeSRGAN). We
adopt a tailored architecture of the original SRGAN and model quantization to
boost the execution on CPU and Edge TPU devices, achieving up to 200 fps
inference. We further optimize our model by distilling its knowledge to a
smaller version of the network and obtain remarkable improvements compared to
the standard training approach. Our experiments show that our fast and
lightweight model preserves considerably satisfying image quality compared to
heavier state-of-the-art models. Finally, we conduct experiments on image
transmission with bandwidth degradation to highlight the advantages of the
proposed system for mobile robotic applications.
