Depth　estimation　from　a　single　image　is　a　fundamental　problem　in　the　field　of　computer　vision.　With　the　great　success　of　deep　learning　techniques,　various　self-supervised　monocular　depth　estimation　methods　using　encoder-decoder　architectures　have　emerged.　However,　most　previous　approaches　regress　the　depth　map　directly　using　a　single　encoder-decoder　structure,　which　may　not　obtain　sufficient　features　in　the　image　and　results　in　a　depth　map　with　low　accuracy　and　blurred　details.　To　improve　the　accuracy　of　self-supervised　monocular　depth　estimation,　we　propose　a　simple　but　very　effective　scheme　for　depth　estimation　using　a　dual　encoder-decoder　structure　network.　Specifically,　we　introduce　a　novel　global　feature　extraction　network　(GFN)　to　extract　global　features　from　images.　GFN　includes　PoolAttentionFormer　and　ResBlock,　which　work　together　to　extract　and　fuse　hierarchical　global　features　into　the　depth　estimation　network　(DEN).　To　further　improve　the　accuracy,　we　design　two　feature　fusion　mechanisms,　including　global　feature　fusion　and　multiscale　fusion.　The　experimental　results　of　various　dual　encoder-decoder　combination　schemes　tested　on　the　KITTI　dataset　show　that　our　proposed　one　is　effective　in　improving　the　accuracy　of　self-supervised　monocular　depth　estimation,　which　reached　89.6%　(delta　＜　1.25).