Research on Image Super-Resolution Reconstruction Technology Based on Unsupervised Learning

• Published in: International Journal of Aerospace Engineering Vol. 2023; pp. 1 - 12
• Main Authors: Han, Shuo, Mo, Bo, Zhao, Jie, Pan, Bolin, Wang, Yiqi
• Format: Journal Article
• Language: English
• Published: New York Hindawi 21.11.2023; John Wiley & Sons, Inc; Hindawi Limited; Wiley
• Subjects: Accuracy; Aerospace engineering; Algorithms; Data mining; Datasets; Deep learning; Design; Drone aircraft; Equipment and supplies; Generative adversarial networks; Image degradation; Image processing; Image reconstruction; Image resolution; Machine learning; Missiles; Self-similarity; Sensors; Supervised learning; Unsupervised learning
• DOI: 10.1155/2023/8860842
• ISSN: 1687-5966

Affected by the movement of drones, missiles, and other aircraft platforms and the limitation of the accuracy of image sensors, the obtained images have low-resolution and serious loss of image details. Aiming at these problems, this paper studies the image super-resolution reconstruction technology. Firstly, a natural image degradation model based on a generative adversarial network is designed to learn the degradation relationship between image blocks within the image; then, an unsupervised learning residual network is designed based on the idea of image self-similarity to complete image super-resolution reconstruction. The experimental results show that the unsupervised super-resolution reconstruction algorithm is equivalent to the mainstream supervised learning algorithm under ideal conditions. Compared to mainstream algorithms, this algorithm has significantly improved its various indicators in real-world environments under nonideal conditions.