Depth map super-resolution via multiclass dictionary learning with geometrical directions

Depth cameras have gained significant popularity due to their affordable cost in recent years. However, the resolution of depth map captured by these cameras is rather limited, and thus it hardly can be directly used in visual depth perception and 3D reconstruction. In order to handle this problem, we propose a novel multiclass dictionary learning method, in which depth image is divided into classified patches according to their geometrical directions and a sparse dictionary is trained within each class. Different from previous SR works, we build the correspondence between training samples and their corresponding register color image via sparse representation. We further use the adaptive autoregressive model as a reconstruction constraint to preserve smooth regions and sharp edges. Experimental results demonstrate that our method outperforms state-of-the-art methods in depth map super-resolution in terms of both subjective quality and objective quality.