Captured　images　of　outdoor　scenes　usually　exhibit　low　visibility　in　cases　of　severe　haze,　which　interferes　with　optical　imaging　and　degrades　image　quality.　Most　of　the　existing　methods　solve　the　single-image　dehazing　problem　by　applying　supervised　training　on　paired　images;　however,　in　practice,　the　pairing　of　real-world　images　is　not　viable.　Additionally,　the　processing　speed　of　individual　dehazing　models　is　important　in　practical　applications.　In　this　study,　a　novel　unsupervised　single　image　dehazing　network　(USID-Net)　based　on　disentangled　representations　without　paired　training　images　is　explored.　Furthermore,　considering　the　trade-off　between　performance　and　memory　storage,　a　compact　multi-scale　feature　attention　(MFA)　module　is　developed,　integrating　multi-scale　feature　representation　and　attention　mechanism　to　facilitate　feature　representation.　To　effectively　extract　haze　information,　a　mechanism　referred　to　as　OctEncoder　is　designed　to　include　multi-frequency　representations　that　can　capture　more　global　information.　Extensive　experiments　show　that　USID-Net　achieves　competitive　dehazing　results　and　a　relatively　high　processing　speed　compared　to　state-of-the-art　methods.　The　source　code　is　available　at　https://github.com/dehazing/USID-Net.