Hazy　weather　brings　a　lot　of　inconvenience　to　peoples　lives,　such　as　transportation.　Therefore,　image　dehazing　is　still　an　important　focus　point.　To　achieve　image　dehazing,　we　proposed　a　Deep　Dilated　Residual　Haze　Network　(DDRHNet)　based　on　self-encoder　and　self-decoder.　The　proposed　mode　is　an　end-to-end　supervised　method,　which　directly　estimates　image　dehazing　result　instead　of　estimating　the　atmospheric　light　and　the　transmission　in　the　unsupervised　and　classical　atmospheric　scattering　model.　The　DDRHNet　method　obtains　image　dehazed　result　by　finding　the　difference　between　the　input　hazy　image　and　the　output　of　haze-free　image.　Experimental　results　on　an　open　image　dehazing　dataset　called　SOTS　demonstrate　the　superiority　of　the　proposed　DDRHNet　network.　©　2019　IEEE.