Deep-learning-based super-resolution image processing system and method, and computer program for same

Abstract

A deep learning-based super-resolution image processing system may comprise an unmanned aerial vehicle system configured to receive an image captured by an unmanned aerial vehicle and allocate the received image data to a transmission queue for transmission; and a Mobile Edge Computing (MEC) center including one or more transceivers each having a reception queue, and configured to receive the image data from the unmanned aerial vehicle system through the one or more transceivers and generate a super-resolution image corresponding to the image data through a deep learning computation based on a super-resolution model. The unmanned aerial vehicle system may determine which transceiver to transmit the image data based on a data amount difference between the transmission queue and the reception queue. In addition, the MEC center may determine which super-resolution model to apply to the image data such that a time-averaged super-resolution performance defined by Lyapunov optimization is maximized using a quality evaluation value when the super-resolution model has been applied to the image data and a queue stability value of the reception queue.