Lightweight and Dynamic Deblurring for IoT-Enabled Smart Cameras

Abstract

Blurred images often significantly influence the stability of computer vision systems. With the development of the Internet of Things (IoT), a camera that leverages artificial intelligence and edge computing (hereafter referred to as an edge camera) can enhance the robustness of an IoT service. Although previous studies have used deep neural networks (DNNs) for image deblurring, existing image-deblurring techniques result in over-smoothing, even with a lightweight model design. In addition, previous deblurring studies have not considered the quality of the input image first; thus, a clear input image would be processed with unnecessary deblurring. Therefore, our study proposes a lightweight and dynamic border-enhancement deblurring deep network that can be operated with high performance on edge cameras, and the network can assess the quality of input images to determine whether deblurring is necessary. The experimental results show that our lightweight deblurring approach outperforms existing studies by up to 8%, given that the speed also improved by 33.78%. As for the dynamic deblurring approach, our results show that the image metrics can be slightly improved and that speed is improved by 294.5%.