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

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 resear...

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Bibliographic Details
Published inApplied sciences Vol. 14; no. 17; p. 7620
Main Authors Xu, Zhaofei, Gao, Jie, Wang, Xianghui, Kang, Chong
Format Journal Article
LanguageEnglish
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
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14177620