Lightweight Image De-raining for IoT-enabled Cameras
Bo-En Shao, Ching-Hu Lu, Shih-Shinh Huang · 2019
A traditional surveillance system with image enhancement often heavily relies on cloud-tier processing, which demands higher bandwidth and increases latency. To reduce the bottleneck on the cloud and bandwidth under raining weather, we propose a novel image de-raining network for recourse-constrained IoT-enabled cameras by leveraging edge intelligence. The experiment results show that our network is capable of de-raining under heavy-rain conditions, and achieves faster processing time compared with previous researches.