In　our　engineering　application,　it　is　necessary　to　up-scale　low-resolution　infrared　images　to　the　size　of　high-resolution　visible　light　images.　We　have　studied　the　latest　single　image　superresolution　technology　based　on　deep　neural　networks(CNNs)　and　found　that　there　are　two　main　practices　to　improve　superresolution　performance:　increasing　the　depth　of　the　network　and　applying　attention　mechanisms.　Therefore,　we　design　a　channel　attention　interaction(CAI)　module　at　the　head　of　the　network　and　channel　attention　feature　groups(RCAFGS)　based　on　the　progressive　feature　fusion(PFF)　strategy　in　the　network　backbone,　which　can　ease　the　gradient　vanishing　problem　caused　by　increasing　the　depth　and　treat　channel　features　wisely.　And　we　adopt　the　scale-Aware　feature　adaptation　module　and　scaleaware　upsampling　module　for　tackling　the　problem　of　the　non-integer　asymmetric　size.　Finally,　we　propose　one　scale-Arbitrary　infrared　super-resolution　network　based　on　channel　attention　mechanisms.　©　2023　IEEE.