  The ability of generative models to accurately fit data distributions has
resulted in their widespread adoption and success in fields such as computer
vision and natural language processing. In this chapter, we provide a brief
overview of the application of generative models in the domain of infrared (IR)
image super-resolution, including a discussion of the various challenges and
adversarial training methods employed. We propose potential areas for further
investigation and advancement in the application of generative models for IR
image super-resolution.
