A Survey on Image Segmentation and Super-resolution Reconstruction in Visual Sensor Networks

With the rapid development of large-scale sensor networks, visual sensor networks (VSNs) have attracted significant interest from academia and industry. VSNs can transmit more information and allow for more fine-grained monitoring of objects than conventional methods. In this survey, we provide a comprehensive review on image segmentation and super-resolution reconstruction, two key image processing tasks for VSNs, since these two tasks can effectively improve the performance and balance key factors for the performance of VSNs (e.g., network bandwidth, computing resources, and sensor battery life). We begin by expounding the basic concepts and an overall framework of VSNs. Furthermore, we examine state-of-the-art approaches and provide a new taxonomy of existing research topics. Finally, we outline several challenges, possible solutions, and future research directions of these two key image processing tasks for VSNs.