Image　dehazing　has　received　extensive　research　attention　as　images　collected　in　hazy　weather　are　limited　by　low　visibility　and　information　dropout.　Recently,　disentangled　representation　learning　has　made　excellent　progress　in　various　vision　tasks.　However,　existing　networks　for　low-level　vision　tasks　lack　efficient　feature　interaction　and　delivery　mechanisms　in　the　disentanglement　process　or　an　evaluation　mechanism　for　the　degree　of　decoupling　in　the　reconstruction　process,　rendering　direct　application　to　image　dehazing　challenging.　We　propose　a　self-guided　disentangled　representation　learning　(SGDRL)　algorithm　with　a　self-guided　disentangled　network　to　realize　multi-level　progressive　feature　decoupling　through　sharing　and　interaction.　The　self-guided　disentangled　(SGD)　network　extracts　image　features　using　the　multi-layer　backbone　network,　and　attribute　features　are　weighted　using　the　self-guided　attention　mechanism　for　the　backbone　features.　In　addition,　we　introduce　a　disentanglement-guided　(DG)　module　to　evaluate　the　degree　of　feature　decomposition　and　guide　the　feature　fusion　process　in　the　reconstruction　stage.　Accordingly,　we　develop　SGDRL-based　unsupervised　and　semi-supervised　single　image　dehazing　networks.　Extensive　experiments　demonstrate　the　superiority　of　the　proposed　method　for　real-world　image　dehazing.　The　source　code　is　available　at　https://github.com/dehazing/　SGDRL.