Edge-Assisted Deep Video Denoising and Super-Resolution for Real-Time Surveillance at Night

Liming Ge, Wei Bao, Dong Yuan, Bing Bing Zhou

Abstract: Video surveillance cameras have been extensively deployed over the last few years. In case of incidents such as natural disaster rescue, it provides vital guidance in real-time. However, due to the limited camera hardware and network bandwidth, noise are observed especially at night and the resolution is low. To tackle these two issues, we design and implement EADV, an Edge-Assisted Deep Video denoising and super-resolution system for real-time surveillance at night. We demonstrate the video quality enhancement using a camera, a displayer, and an edge server. The low-quality video captured by the camera is enhanced by the server and shown on the displayer. The enhanced real-time video is smooth and the performance uplift is observable.