Infrared　images　hold　significant　value　in　applications　such　as　remote　sensing　and　fire　safety.　However,　infrared　detectors　often　face　the　problem　of　high　hardware　costs,　which　limits　their　widespread　use.　Advancements　in　deep　learning　have　spurred　innovative　approaches　to　image　super-resolution　(SR),　but　comparatively　few　efforts　have　been　dedicated　to　the　exploration　of　infrared　images.　To　address　this,　we　design　the　Residual　Swin　Transformer　and　Average　Pooling　Block　(RSTAB)　and　propose　the　SwinAIR,　which　can　effectively　extract　and　fuse　the　diverse　frequency　features　in　infrared　images　and　achieve　superior　SR　reconstruction　performance.　By　further　integrating　SwinAIR　with　U-Net,　we　propose　the　SwinAIR-GAN　for　real　infrared　image　SR　reconstruction.　SwinAIR-GAN　extends　the　degradation　space　to　better　simulate　the　degradation　process　of　real　infrared　images.　Additionally,　it　incorporates　spectral　normalization,　dropout,　and　artifact　discrimination　loss　to　reduce　the　potential　image　artifacts.　Qualitative　and　quantitative　evaluations　on　various　datasets　confirm　the　effectiveness　of　our　proposed　method　in　reconstructing　realistic　textures　and　details　of　infrared　images.　©　2024　by　the　authors.