Rainy　weather　presents　significant　challenges　for　applications　relying　on　visual　perception　in　intelligent　transportation　systems.　The　scarcity　of　real　paired　training　data　complicates　single-image　rain　removal　tasks,　prompting　an　increasing　interest　in　unsupervised　methods　capable　of　handling　real-world　rainy　images　without　paired　data.　At　present,　most　unsupervised　rain　removal　methods　are　based　on　the　CycleGAN　framework;　however,　the　combination　of　this　framework　and　transformer　is　not　satisfactory　owing　to　most　Transformers＇　insufficient　ability　to　model　real　rain　features　with　global　inhomogeneous　distributions,　which　prevents　them　from　being　fully　applicable　to　unsupervised　tasks.　This　study　devised　an　unsupervised　rain　removal　network　based　on　CycleGAN　and　the　DerainFormer　transformer.　First,　a　deformable　sparse　attention　mechanism　was　developed　to　improve　the　Transformer＇s　suitability　for　unsupervised　tasks　in　CycleGAN　architectures.　Subsequently,　a　two-stage　alternating　transformer　structure　was　designed　to　enhance　its　global　non-uniform　modeling　capabilities　for　real　rain　images,　In　addition,　a　dual-channel　parallel　feed-forward　network　was　used　to　establish　the　correlation　between　multiscale　rain　stripes.　Finally,　since　rain　removal　is　considered　a　decomposition　task,　a　rain　layer　unsupervised　training　method　for　joint　positional　contrastive　learning　was　proposed　to　separate　the　rain　streaks　effectively.　We　conducted　several　experiments　on　different　real　and　synthetic　rain　datasets　and　the　results　confirmed　that　our　unsupervised　rain　removal　method　performed　well.　The　source　code　will　be　released　at　https://github.com/derainsipl/CycFormer.