Captured　outdoor　scene　images　are　easily　affected　by　haze.　Most　image　dehazing　methods　have　limited　generalization　capabilities　for　real-world　hazy　images　owing　to　the　complexities　of　real-world　environments　and　domain　gaps　in　the　training　datasets.　This　paper　proposes　a　semi-supervised　single-image　dehazing　network　based　on　disentangled　meta-knowledge.　The　symmetric　and　heterogeneous　design　of　the　disentangled　network　is　conducive　to　the　separation　of　the　content　and　mask　features　of　hazy　images　and　these　features　are　used　as　meta-knowledge　to　guide　feature　fusion　in　the　dehazing　network.　Moreover,　functions　describing　constant-color　and　disentangled-reconstruction-checking　losses　are　designed　to　ensure　the　subjective　qualities　of　the　generated　dehazed　images.　The　results　of　extensive　experiments　conducted　on　synthetic　datasets　and　real-world　images　indicate　that　the　proposed　algorithm　outperforms　state-of-the-art　single-image　dehazing　algorithms.　In　addition,　the　algorithm　effectively　improves　the　performance　of　object-detection　tasks.　The　source　code　is　publicly　available　at　https://github.com/dehazing/DNDM.　IEEE