The　performance　of　the　emerging　infrared　polarization　remote　sensing　systems　is　limited　by　the　use　of　infrared　polarization　imaging　sensors　and　cannot　produce　high-resolution　(HR)　infrared　polarization　images.　The　lack　of　HR　infrared　polarization　imaging　sensors　and　systems　hinders　the　development　and　application　of　infrared　polarization　imaging　technology.　The　existing　infrared　image　super-resolution　(SR)　methods　fail　to　improve　the　resolution　of　infrared　polarization　images　(IRPIs)　while　preserving　the　infrared　polarization　information　inherent　in　the　IRPIs;　thus,　aiming　at　obtaining　accurate　HR　infrared　polarization　images,　this　study　proposed　a　deep-learning-based　SR　method,　SwinIPISR,　to　improve　infrared　polarization　image　resolution　and　preserve　the　infrared　polarization　information　of　the　target　or　scene.　The　performance　of　the　proposed　SwinIPISR　was　verified　and　compared　with　existing　SR　methods.　In　contrast　to　other　methods,　SwinIPISR　not　only　improves　image　resolution　but　also　retains　polarization　information　of　the　scene　and　objects　in　the　polarization　image.　Further,　the　impact　of　the　network　depth　of　SwinIPISR　on　the　SR　performance　was　evaluated　through　experiments.　The　experimental　results　confirmed　the　effectiveness　of　the　proposed　SwinIPISR　in　enhancing　the　image　resolution　and　visual　effects　of　infrared　polarization　images　without　compromising　the　polarization　information.