Infrared Image Super-Resolution: Systematic Review, and Future Trends
It synthesizes existing knowledge for researchers in computer vision and image processing, but is incremental as it does not introduce new methods.
This survey provides a comprehensive review of infrared image super-resolution, covering applications, hardware dilemmas, methodologies, datasets, and evaluation metrics, while highlighting current deficiencies and future directions.
Image Super-Resolution (SR) is essential for a wide range of computer vision and image processing tasks. Investigating infrared (IR) image (or thermal images) super-resolution is a continuing concern within the development of deep learning. This survey aims to provide a comprehensive perspective of IR image super-resolution, including its applications, hardware imaging system dilemmas, and taxonomy of image processing methodologies. In addition, the datasets and evaluation metrics in IR image super-resolution tasks are also discussed. Furthermore, the deficiencies in current technologies and possible promising directions for the community to explore are highlighted. To cope with the rapid development in this field, we intend to regularly update the relevant excellent work at https://github.com/yongsongH/Infrared_Image_SR_Survey.