Single　image　desnowing　is　an　important　and　challenge　task　for　lots　of　computer　vision　applications,　such　as　visual　tracking　and　video　surveillance.　Although　existing　deep　learning-based　methods　have　achieved　promising　results,　most　of　them　rely　on　the　local　deep　features　and　neglect　global　relationship　information　between　the　local　regions.　Therefore,　inevitably　leading　to　over-smooth　or　detail　loss　results.　To　solve　this　issue,　we　design　a　UNet-based　end-to-end　architecture　for　image　desnowing.　Specially,　to　better　characterize　global　information　and　preserve　image　detail,　we　combine　Window-based　Self-Attention　(WSA)　transformer　block　with　Residue　Spatial　Attention　(RSA)　to　build　basic　unit　of　our　network.　Besides,　to　protect　the　structure　of　the　image　effectively,　we　also　introduce　a　Residue　Channel　(RC)　loss　to　guide　high-quality　image　restoration.　Extensive　experimental　results　on　both　synthetic　and　real-world　datasets　demonstrate　that　the　proposed　model　achieves　new　state-of-the-art　results.