Convolutional　neural　network　(CNN)-based　single　image　super-resolution　(SR)　methods　have　achieved　superior　performance　on　some　discrete　scale　factors.　Majority　CNN-base　methods　only　work　for　some　integer　scale　factors,　such　as　2,　3,　and　4.　Meta-SR　method　manages　to　work　for　more　discrete　scale　factors,　such　as　1.1,　1.2,　etc.　However,　the　scale　factors　for　SR　should　be　arbitrary　in　practical　applications.　To　extend　CNN-based　SR　methods　to　an　arbitrary　scale,　in　this　paper,　we　propose　a　multi-scale　fusion　method　for　arbitrary-scale　SR　(MSFASR).　In　our　MSFASR,　we　first　input　a　low-resolution　(LR)　image　into　an　existing　SR　model　to　generate　two　different　high-resolution　(HR)　images.　Then　we　downscale/upscale　HR　images　to　predict　the　SR　results　with　target　resolution.　By　exploring　scale　factor　preference,　we　combine　the　SR　results　to　get　the　final　SR　image.　Experimental　results　with　Meta-SR　method　demonstrated　that　the　MSFASR　method　could　extend　CNN-based　SR　method　from　discrete　to　continuous　scale　and　achieve　good　SR　performance　on　arbitrary　scales.　©　2019　IEEE.