Super-resolution reconstruction algorithm for aerial image data management based on deep learning

Deep learning aims to learn the internal laws and representation levels of sample data. The information obtained in the learning process is of great help in the interpretation of data such as text, images and sounds. With the continuous development of modern technology, vision-based autonomous navigation technology, as the core of UAV technology, has received extensive attention worldwide. However, in the complex flight environment of drones, the capability of the airborne image sensor is affected by weather factors and defocusing diffraction phenomena, which will result in low image resolution. In response to this problem, this paper studies deep learning super-resolution reconstruction algorithms (SRRAs) for aerial images and proposes a super-resolution reconstruction method based on deep learning. Through the experimental design of the algorithm in this paper, we can conclude that the method proposed in this paper not only increases the sharpness of aerial images by approximately 10% but also increases the speed of image reconstruction by approximately 20% compared with the traditional depth reconstruction algorithm.