Dual Regularization Based Depth Map Super-Resolution with Graph Laplacian Prior

The edge information plays key role in the restoration of depth map. Most conventional methods assume that the RGB-D pairs are consistent in edge areas. In this paper, firstly, we point out that in most cases the consistency between normal map and depth map(N-D pairs) are much higher than that be-tween RGB-D pairs. Then we propose a dual regularization term to guide the restoration of depth map, which constrains the consistency between N-D pairs back and forth. Moreover, a reweighted graph Laplacian prior is incorporated into a unified optimization framework to effectively protect piece-wise smoothness(PWS) characteristics of depth map. By treating depth maps as graph signals, the weight between two nodes is adapted according to its content. Extensive experimental results demonstrate the superior performance of our method compared with other state-of-the-art works in terms of objective and subjective quality evaluations.