基于卷积神经网络的光电导航图像超分辨率方法

本文设计了一种基于卷积神经网络的光电导航图像超分辨率系统，为飞机飞行或着陆、卫星图像提供可靠的导航信息。超清化的可见光图像能够为地面监控人员、飞行员或无人机提供可靠的图像信息。超清化方法可以降低导航系统对硬件设备、计算资源的要求，适用于普通民航客机与搭载低精度摄像头的小型无人机。本文以卷积神经网络作为框架，搭建接近实时计算的图像超分辨率模型。

Photoelectric Navigation Image Super-Resolution Method Based on Convolutional Neural Network

In this paper, a photoelectric navigation image super-resolution system based on convolutional neural network is designed to provide reliable navigation information for aircraft flight or landing and satellite images. Super definition visible images can provide reliable information for ground monitors, pilots or drones. The super definition method can reduce the requirements of navigation system on hardware equipment and computing resources, and is suitable for ordinary civil aircraft and small UAV with low precision cameras. In this paper, the convolutional neural network is used as a framework to build an image super-resolution model for near real-time computing.