Deep　learning　has　been　widely　used　in　single　image　rain　removal　and　demonstrated　favorable　universality.　However,　it　is　still　challenging　to　remove　rain　streaks,　especially　in　the　nightscape　rain　map　which　exists　heavy　rain　and　rain　streak　accumulation.　To　solve　this　problem,　a　single　image　nightscape　rain　removal　algorithm　based　on　Multi-scale　Fusion　Residual　Network　is　proposed　in　this　paper.　Firstly,　based　on　the　motion　blur　model,　evenly　distributed　rain　streaks　are　generated　and　the　dataset　is　recon-structed　to　solve　the　lack　of　nightscape　rain　map　datasets.　Secondly,　according　to　the　characteristics　of　the　night　rain　map,　multi-scale　residual　blocks　are　drawn　on　to　reuse　and　propagate　the　feature,　so　as　to　ex-ploit　the　rain　streaks　details　representation.　Meanwhile,　the　linear　sequential　connection　structure　of　multi-scale　residual　blocks　is　changed　to　a　u-shaped　codec　structure,　which　tackles　the　problem　that　features　cannot　be　extracted　effectively　due　to　insufficient　scale.　Finally,　the　features　of　different　scales　are　com-bined　with　the　global　self-attention　mechanism　to　get　different　rain　streak　components,　then　a　cleaner　re-stored　image　is　obtained.　The　quantitative　and　qualitative　results　show　that,　compared　to　the　existing　algo-rithms,　the　proposed　algorithm　can　effectively　remove　rain　streaks　while　retaining　detailed　information　and　ensuring　the　integrity　of　image　information.　©　2023　Computer　Society　of　the　Republic　of　China.　All　rights　reserved.