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Adaptive regularized noise smoothing of dense range image using directional Laplacian operators

Noise smoothing is very important method in early vision. Recently, many signals such as an intensity image and a range image are widely used in 3D reconstruction, but the observed data are corrupted by many different sources of noise and often need to be preprocessed before further applications. This research proposes a novel adaptive regularized noise smoothing of dense range image using directional Laplacian operators. In general, dense range data includes heavy noise such as Gaussian noise and impulsive noise. Although the existing regularized noise smoothing algorithm can easily smooth Gaussian noise, impulsive noise is not easy to remove from observed range data. In addition, in order to recover the problem such as artifacts on edge region in the conventional regularized noise smoothing of range data, the second smoothness constraint is applied through minimizing the difference between the median filtered data and original data. As a result, the proposed algorithm can effectively remove the noise of dense range data with directional edge preserving.