Depth Restoration: A fast low-rank matrix completion via dual-graph regularization
This work addresses depth restoration for applications like real-world sensing, but it is incremental as it builds on existing low-rank matrix completion techniques.
The paper tackles the problem of depth map restoration from noisy and incomplete data by proposing a fast low-rank matrix completion method with dual-graph regularization, which outperforms state-of-the-art methods in quality evaluations, particularly for severe degradation.
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.