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.

翻译：单图像超分辨率技术可在需要可靠视觉流以监控任务、处理远程操作或研究相关视觉细节的环境中支持机器人任务。本文提出一种用于实时超分辨率的高效生成对抗网络模型，称为EdgeSRGAN（代码见https://github.com/PIC4SeR/EdgeSRGAN）。我们采用原始SRGAN的定制化架构与模型量化技术，提升CPU和边缘TPU设备上的执行效率，推理速度可达200帧/秒。进一步通过知识蒸馏将模型知识迁移至更小版本的网络，相较于标准训练方法取得了显著改进。实验表明，与较重的现有最优模型相比，我们的快速轻量级模型可保持相当令人满意的图像质量。最后，我们针对带宽退化的图像传输进行了实验，凸显了所提系统在移动机器人应用中的优势。