Infrared Image Super-Resolution: A Systematic Review and Future Trends

Image Super-Resolution (SR) is a fundamental task in computer vision and image processing. The challenge of super-resolving infrared (IR) or thermal images represents a key and persistent research frontier in the deep learning era. Different from visible-light SR, IRSR is challenged by the distinct properties of thermal images, which typically exhibit low contrast, limited high-frequency details, and sensor-specific noise. This survey provides a comprehensive review of Infrared Image Super-Resolution (IRSR), covering its applications, the inherent limitations of IR imaging hardware, and a detailed taxonomy of processing methodologies. In addition, we discuss the critical roles of datasets and evaluation metrics in the field. Finally, we highlight current technological gaps and identify promising future directions for the community. To keep pace with the rapid developments, we maintain an actively updated repository of relevant work at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yongsongH/Infrared Image\_SR\_Survey</uri>.