A robust sparse representation framework for depth map restoration

Recently, techniques based on dictionary learning for sparse representation have demonstrated promising results for depth or disparity maps restoration. However, we show that these methods are not robust due to the fact that depth or disparity maps are not only slightly contaminated by additive Gaussian noise but also seriously corrupted with outliers, occlusions, or even variable uncertainties. These seriously corrupted pixels not only lead to irregular structures obtained by dictionary but also seriously deteriorate the sparse coding effectiveness. To overcome these problems, in this paper we propose a new robust sparse representation framework to restore depth maps. In our proposed framework, seriously corrupted pixels can be automatically identified and their disturbance effects are gradually diminished through a few iterations. Thus, our proposed framework is more robust for depth restoration. Experimental results are presented to demonstrate the effectiveness of the proposed framework.