Inf-OSRGAN: Optimized Blind Super-Resolution GAN for Infrared Images

• Published in: Applied sciences Vol. 14; no. 17; p. 7620
• Main Authors: Xu, Zhaofei, Gao, Jie, Wang, Xianghui, Kang, Chong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Algorithms; Datasets; degradation; Design; Heat detection; infrared image; Light; Methods; Neural networks; Night vision; Physical properties; Realism; spatially correlative loss; super-resolution; Surveillance; Target recognition
• DOI: 10.3390/app14177620
• ISSN: 2076-3417

With the widespread application of infrared technology in military, security, medical, and other fields, the demand for high-definition infrared images has been increasing. However, the complexity of the noise introduced during the imaging process and high acquisition costs limit the scope of research on super-resolution algorithms for infrared images, particularly when compared to the visible light domain. Furthermore, the lack of high-quality infrared image datasets poses challenges in algorithm design and evaluation. To address these challenges, this paper proposes an optimized super-resolution algorithm for infrared images. Firstly, we construct an infrared image super-resolution dataset, which serves as a robust foundation for algorithm design and rigorous evaluation. Secondly, in the degradation process, we introduce a gate mechanism and random shuffle to enrich the degradation space and more comprehensively simulate the real-world degradation of infrared images. We train an RRDBNet super-resolution generator integrating the aforementioned degradation model. Additionally, we incorporate spatially correlative loss to leverage spatial–structural information, thereby enhancing detail preservation and reconstruction in the super-resolution algorithm. Through experiments and evaluations, our method achieved considerable performance improvements in the infrared image super-resolution task. Compared to traditional methods, our method was able to better restore the details and clarity of infrared images.