In　this　paper,　we　propose　a　progressive　encoding-decoding　network　(PEDN)　for　image　dehazing.　First,　we　built　a　basic　dehaze　unit　to　progressively　process　the　image　to　achieve　image　dehazing　in　stages.　The　basic　dehaze　unit　is　composed　of　a　feature　memory　module　and　an　encoding-decoding　network.　The　feature　memory　module　is　used　to　transfer　features　at　different　progressive　stages.　The　encoding-decoding　network　is　responsible　for　feature　extraction,　encodes　and　decodes　images　by　fusing　different　levels　of　pyramid　features.　The　basic　dehaze　unit　shares　parameters　during　the　progressive　process,　which　effectively　reduces　the　difficulty　of　network　training　and　improves　the　fitting　speed.　The　proposed　model　is　an　end-to-end　image　dehazing　network,　which　does　not　depend　on　the　atmospheric　scattering　model.　In　addition,　we　extracted　the　depth　information　of　the　hazy　image　and　obtained　its　pyramid　features,　and　incorporated　the　depth　information　into　the　feature　extraction　to　guide　the　network　to　restore　clear　images　more　accurately.　Experiments　show　that　the　our　method　not　only　performs　well　on　synthetic　datasets,　but　also　has　excellent　performance　on　real　world　hazy　images.　It　is　superior　to　current　image　dehaze　methods　in　quantitative　indexes　and　visual　perception.　Code　has　been　made　available　at　https://github.com/LWQDU/PEDN.