The　image　raindrop　removal　task　aims　to　remove　raindrops　attached　to　the　lens　from　a　given　rainy　image,　restoring　a　clean　image,　which　plays　a　crucial　role　in　downstream　tasks　of　computer　vision.　Because　the　existing　image　raindrop　removal　methods　not　taking　the　spatial　locality　and　scale　diversity　of　raindrops　into　account,　the　raindrops　are　usually　not　completely　removed　from　the　images.　To　alleviate　the　above　issues,　this　paper　proposes　a　raindrop　removal　method　from　a　single　image　based　on　a　multi-attention　mechanism.　First,　in　order　to　adapt　to　the　spatial　locality　and　scale　diversity　of　raindrops,　a　multi-scale　feature　extraction　module　and　a　multi-attention　module　are　combined　to　construct　an　encoder-decoder　architecture.　The　multi-attention　module　integrates　pixel,　channel,　and　spatial　attention,　which　can　match　the　spatial　locality　of　raindrops　adaptively.　In　addition,　this　paper　designs　an　iterative　image　feature　fusion　module.　The　features　from　the　decoder　and　raindrop　images　are　fused　to　obtain　a　preliminary　raindrop　removal　image;　then,　the　decoder　features　are　enhanced　with　the　preliminary　raindrop　removal　image　to　obtain　further　refined　features;　the　preliminary　raindrop　removal　image　and　refined　features　are　fused　to　obtain　a　final　raindrop　removal　image.　The　experimental　results　on　Raindrop　image　test　set　show　that　the　proposed　method　can　effectively　remove　raindrops　from　the　image　compared　with　other　methods,　further　improving　the　performance　of　raindrop　removal,　and　improving　the　PSNR　by　0.25　dB　compared　to　the　best　method　in　the　comparison　methods.　©　2025　Institute　of　Computing　Technology.　All　rights　reserved.