Types　of　snow　degradation　are　complex　and　diverse.　Snow　removal　often　requires　the　construction　of　sufficient　visual　representations.　Although　convolution-based　methods　perform　well　in　local　perception,　they　struggle　to　model　globally.　On　the　other　hand,　methods　based　on　self-attention　can　capture　long-range　dependencies　but　often　overlook　local　information　and　texture　details.　In　this　paper,　we　proposed　a　hybrid　network　called　WaveFrSnow,　aimed　at　enhancing　the　performance　of　single-image　snow　removal　by　combining　the　advantages　of　convolution　and　cross-attention.　Firstly,　we　introduced　a　frequency-separation　cross-attention　mechanism　based　on　wavelet　transform　(WaveFrSA)　to　enhance　the　global　and　texture　representations　of　snow　removal.　Specifically,　frequency-separated　attention　perceives　the　texture　in　the　high-frequency　branch,　captures　global　information　in　the　low-frequency　branch,　and　introduces　convolution　to　obtain　local　features.　In　addition,　we　constructed　local　representations　through　efficient　convolutional　encoder　branches.　Furthermore,　we　develop　a　Multi-Scale　Degradation　Aggregation　(MSDA)　module　to　integrate　rich　implicit　degradation　features.　Based　on　the　MSDA　module,　a　Degradation　Area　Restoration　(DAR)　network　is　constructed,　aiming　to　achieve　high-quality　image　restoration　following　the　snow　removal　process.　Taken　together,　comprehensive　experimental　results　on　multiple　publicly　available　datasets　demonstrate　the　superiority　of　the　proposed　method　over　the　state-of-the-art　method.　Additionally,　the　desnowing　results　effectively　improve　the　accuracy　of　downstream　vision　tasks.　The　code　and　datasets　in　this　study　are　available　at　https://github.com/dxw2000/WaveFrSnow.　©　2024　Elsevier　Inc.