Environment-Aware Multiscene Image Enhancement for Internet of Things Enabled Edge Cameras

Abstract

With the development of the Internet of Things, it is feasible to leverage as much computing resources on an IoT-enabled camera to realize edge intelligence (hereafter referred to as an edge camera) for providing various human-centered services. However, environmental interference can significantly degrade the visual quality of an image into edge cameras, thereby affecting the overall image and service quality. In order to maintain image stability, image enhancement using complex deep networks has been widely utilized. As edge cameras often have restricted computing power and cannot run complex neural networks efficiently, we design a lightweight residual enhancement network, which incorporates highly efficient convolution operations as basic building blocks. With this modular design on a generative adversarial network training framework, the resultant network can be easily optimized according to hardware specifications of a given edge camera. To accommodate various weather conditions for an edge camera, we further design an environment-aware model deployer that can detect the weather conditions and deploy the most suitable image-enhancement model. The experimental results show that the lightweight residual enhancement network can produce image quality close to what past research demonstrated while increasing running speeds by more than 30%. In a streaming video to simulate changing weather, the results also demonstrate the effectiveness of the proposed environment-aware model deployer to maintain the quality of the images under a dynamic environment.