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Infrared Image Super-Resolution via GAN

arXiv 2312.00689 ↗First posted 1 Dec 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 65 references that are in the map. First posted on arXiv 1 Dec 2023.

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29 of 65 references.

References · 29

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17 more
  1. 2019Feedback Network for Image Super-Resolutioncited by 155
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  3. 2021ViTGAN: Training GANs with Vision Transformerscited by 110
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  7. 2021Pyramid Adversarial Training Improves ViT Performancecited by 39
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  9. 2021Toward Real-World Super-Resolution via Adaptive Downsampling Modelscited by 14
  10. 2022Infrared Image Super-Resolution: Systematic Review, and Future Trendscited by 11
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  12. 2022Joint Learning Content and Degradation Aware Feature for Blind Super-Resolutioncited by 8
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  16. 2022Rethinking Degradation: Radiograph Super-Resolution via AID-SRGANcited by 4
  17. 2023Texture and Noise Dual Adaptation for Infrared Image Super-Resolutioncited by 2

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