Depth estimation from a single image is a significant and challenging task in computer vision. It is difficult to represent the correspondence between depth and RGB image without any prior information. Unlike previous approaches that only map the RGB images to the corresponding depth locally, we propose to combine the local depth with region-level and global scene structures. Firstly, the global layout is retrieved from similar images. Secondly, local depth estimated by the coupled dictionary learning (DL) formulation is combined with the global layout to maintain the global result. Finally, in order to further refine the depth estimated, we propose to detect the sky region in outdoor scene. In addition, several edge-preserving strategies are taken to clearly distinguish different objects. The experimental results demonstrate that our method represents a more realistic depth map than other methods on the popular public dataset Make3D.