Foreground Information-Aware Image Superresolution Reconstruction for Image Processing IoT Systems in Smart City

Abstract

In recent years, with the rise of Internet of Things (IoT), a majority of smart technologies, such as autonomous vehicles, smart healthcare, and urban surveillance, require a huge number of images of high quality and resolution. Currently, image superresolution reconstruction technologies are widely used for obtaining high quality images. Unfortunately, the existing methods generally focus on the whole image without highlighting foreground information and lack visual focus. Also, they have low utilization of shallow features and numerous training parameters. In this paper, we propose a feature extraction module that focuses on foreground information: the parallel attention module (PAM). PAM computes channel and spatial attention in parallel, inputs the obtained attention values into a cascaded gated network, and dynamically adjusts the weights of both using nonuniform joint loss to focus on image foreground information and detail features to improve the reconstructed image’s foreground sharpness. To further improve the performance, we propose to connect multiple PAM modules in series with skip connections and call it PAMNet. PAMNet can better leverage the shallow residual features, and the reconstructed images are closer to ground truth. Thereby, the applications in the urban image processing IoT systems can obtain high-resolution images more quickly and precisely. The comprehensive experimental results show that PAMNet performs better than the state-of-the-art technologies.