Single Depth Map Super-resolution with Local Self-similarity

Consumer depth sensors such as time-of-flight camera or Kinect have gained significant popularity in recently. However, the captured depth maps suffer from limited spatial resolution and a variety of noise, making such depth maps difficult to be directly applied in related applications. In this paper, we present a novel single depth map super-resolution method, aiming to reconstruct high-resolution depth map from its associated low-resolution depth map without any auxiliary information. Particularly, we exploit the depth local self-similarity to assist in constructing patch pairs in terms of high-resolution and low-resolution depth edge patches, and then deduce a high-resolution depth edge map via Markov model. Finally, we implement a joint bilateral filter to reconstruct the high-resolution depth map. Experimental results show that our method overcomes existing methods on the benchmark database as well as Kinect captured depth maps.