In order to improve theresolution of depth imagemore effectively, a deeper convolution neural network is constructed in this paper. The network directly adapts the lowresolution depth image as the initial input of the network,and learns the highorder representation of depth image through the convolution neural network to obtain the features with more expressive ability.At the same time,the subpixel convolution layer is introduced at the output layer of the network. Based on the extracted features, a set of sampling filter is learned to achieve the amplification operation. For a better performance of the convergence, the residual network is added to our network. The experimentsare conducted on four commonly used datasets, and the results show that our network is faster than other advanced ones at the convergence rate. The proposed method can effectively protect the edge structure of the depth image,solve the artifact problem,and reachesgreat performance both in qualitative and quantitative aspects.