  As a real scenes sensing approach, depth information obtains the widespread
applications. However, resulting from the restriction of depth sensing
technology, the depth map captured in practice usually suffers terrible noise
and missing values at plenty of pixels. In this paper, we propose a fast
low-rank matrix completion via dual-graph regularization for depth restoration.
Specifically, the depth restoration can be transformed into a low-rank matrix
completion problem. In order to complete the low-rank matrix and restore it to
the depth map, the proposed dual-graph method containing the local and
non-local graph regularizations exploits the local similarity of depth maps and
the gradient consistency of depth-color counterparts respectively. In addition,
the proposed approach achieves the high speed depth restoration due to
closed-form solution. Experimental results demonstrate that the proposed method
outperforms the state-of-the-art methods with respect to both objective and
subjective quality evaluations, especially for serious depth degeneration.
