Infrared Image Super-Resolution via GAN

Yongsong Huang, Shinichiro Omachi
12/1/2023
eess.IVcs.CV

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Abstract

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.

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Cite this paper

@article{huang2023infrared,
  title  = {Infrared Image Super-Resolution via GAN},
  author = {Yongsong Huang and Shinichiro Omachi},
  year   = {2023},
  eprint = {2312.00689},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.00689v1}
}

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