Rainy　images　can　affect　the　performance　and　accuracy　of　computer　vision　tasks.　Rainy　images　often　contain　raindrops　or　rain　marks　from　different　directions，sizes，and　shapes.　When　removing　these　raindrops　and　rain　marks，the　existing　methods　often　do　not　take　into　account　the　feature　information　of　rainy　images　at　different　fine　scales，and　only　use　a　single　scale.　There　is　a　big　defect　in　image　deraining，and　it　is　impossible　to　restore　a　clear　enough　image　for　visual　tasks.　Therefore，benefiting　from　the　powerful　feature　extraction　capability　of　the　convolutional　neural　network　architecture，an　end-to-end　multi-cascade　progressive　convolution　structure　operator　is　proposed，which　consists　of　four　convolutional　layers　connected　through　a　ladder　to　form　an　overall　module.　This　module　can　extract　and　integrate　rainy　weather　features　in　multi-scale　scenes.　The　operator　module　is　embedded　into　the　progressive　recurrent　network　structure，the　recurrent　structure　is　used　to　remove　rain　streaks　many　times，and　finally　the　rain-free　image　close　to　the　real　image　is　effectively　restored.　The　method　is　compared　with　the　existing　artificially　synthesized　rain　image　datasets　Rain100H，Rain100L，Rain800　and　the　synthetic　rain　image　dataset　BDD1000　in　the　field　of　automatic　driving.　The　experiment　results　shows　that　the　PSNR　values　of　the　algorithm　on　the　four　datasets　reach　30.　70　，37.　91，27.　63，35.　74　dB，and　the　SSIM　values　reach　0.　914，0.　980，0.　894，0.　977.　Through　the　visual　display　of　the　rain　removal　results　of　the　real　rain　map　dataset，the　effectiveness　of　the　method　in　this　paper　on　the　rain　removal　task　is　fully　verified.　©　2023,　Science　Press.　All　rights　reserved.