The　key　issue　in　monocular　depth　estimation　is　how　to　construct　the　depth　image　better　and　improve　the　quality　of　the　depth　map.　At　present,　most　of　the　monocular　depth　estimation　methods　based　on　deep　learning　manipulate　images　at　low　resolution　that　leads　to　loss　of　detail　and　blurring　of　boundaries.　Nevertheless,　deep　learning　with　a　large　number　of　parameters　needs　highly　computational　complexity,　which　makes　it　difficult　to　apply　high-resolution　(HR)　images　to　the　depth　estimate.　In　this　work,　model　accuracy　and　runtime　are　two　important　factors　to　be　considered.　To　improve　the　depth　map　quality　and　reduce　the　running　time　of　the　network,　we　introduce　super-resolution　techniques　as　methods　of　up-sampling　to　generate　high-quality　depth　images　at　a　faster　rate　for　the　depth　estimation　network.　A　novel　approach　is　proposed　for　collecting　high-level　features　that　are　captured　under　different　receptive　fields.　The　gated　multi-scale　decoder　allows　us　to　effectively　filter　information　by　the　gated　module.　By　combining　the　gated　module　to　aid　the　super　resolution　of　depth　images,　our　method　reduces　memory　consumption　while　improves　reconstruction　quality.　Experiment　results　on　the　challenging　NYU　Depth　v2　dataset　demonstrate　that　both　contributions　provide　significant　performance　gains　over　the　state-of-the-art　in　self-supervised　depth　estimation.