Joint-Feature Guided Depth Map Super-Resolution With Face Priors

In this paper, we present a novel method to super-resolve and recover the facial depth map nicely. The key idea is to exploit the exemplar-based method to obtain the reliable face priors from high-quality facial depth map to improve the depth image. Specifically, a new neighbor embedding (NE) framework is designed for face prior learning and depth map reconstruction. First, face components are decomposed to form specialized dictionaries and then reconstructed, respectively. Joint features, i.e., low-level depth, intensity cues and high-level position cues, are put forward for robust patch similarity measurement. The NE results are used to obtain the face priors of facial structures and smooth maps, which are then combined in an uniform optimization framework to recover high-quality facial depth maps. Finally, an edge enhancement process is implemented to estimate the final high resolution depth map. Experimental results demonstrate the superiority of our method compared to state-of-the-art depth map super-resolution techniques on both synthetic data and real-world data from Kinect.