Super Resolution for Augmented Reality Applications
Vladislav Li, George Amponis, Jean‐Christophe Nebel, Vasileios Argyriou, Θωμάς Λάγκας, Savvas Ouzounidis, Panagiotis G. Sarigiannidis · IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · 2022
Latest developments in machine learning (ML), adversarial networks, combined with increasingly powerful IoT devices via the introduction of efficient processors, are bringing about the implementation of near real-time object detection and classification for augmented reality (AR) and virtual reality (VR) applications. This paper intends to explore new object detection and classification technologies leveraging super-resolution (SR), that have the potential to be integrated into small, mobile and low-power ARNR devices. SR in conjunction with novel object detection and classification algorithms are examined in the presented paper, with the ultimate goal of proposing a low-footprint Generative Adversarial Network (GAN)-based framework capable of receiving an LR input and outputting an SR-supported recognition model based on FRRCNN, YOLOv3 or Retina.