Although　deep　convolutional　neural　networks　(DCNN)　show　significant　improvement　for　single　depth　map　(SD)　super-resolution　(SR)　over　the　traditional　counterparts,　most　SDSR　DCNNs　do　not　reuse　the　hierarchical　features　for　depth　map　SR　resulting　in　blurred　high-resolution　(HR)　depth　maps.　They　always　stack　convolutional　layers　to　make　network　deeper　and　wider.　In　addition,　most　SDSR　networks　generate　HR　depth　maps　at　a　single　level,　which　is　not　suitable　for　large　up-sampling　factors.　To　solve　these　problems,　we　present　pyramid-structured　depth　map　super-resolution　based　on　deep　dense-residual　network.　Specially,　our　networks　are　made　up　of　dense　residual　blocks　that　use　densely　connected　layers　and　residual　learning　to　model　the　mapping　between　high-frequency　residuals　and　low-resolution　(LR)　depth　map.　Furthermore,　based　on　the　pyramid　structure,　our　network　can　progressively　generate　depth　maps　of　various　levels　by　taking　advantages　of　features　from　different　levels.　The　proposed　network　adopts　a　deep　supervision　scheme　to　reduce　the　difficulty　of　model　training　and　further　improve　the　performance.　The　proposed　method　is　evaluated　on　Middlebury　datasets　which　shows　improved　performance　compared　with　6　state-of-the-art　methods.