Solidarity Filter for Noise Reduction of 3D Edges in Depth Images

Abstract

3D applications processing depth images significantly benefit from 3D-edge extraction techniques. Intrinsic sensor noise in depth images is largely inherited to the extracted 3D edges. Conventional denoising algorithms remove some of this noise, but also weaken narrow edges, amplify noisy pixels and introduce false edges. We therefore propose a novel solidarity filter for noise removal in 3D edge images without artefacts such as false edges. The proposed filter is defining neighbouring pixels with similar properties and connecting those into larger segments beyond the size of a conventional filter aperture. The experimental results show that the solidarity filter outperforms the median and morphological close filters with $42\,\%$ and $69\,\%$ higher PSNR, respectively. In terms of the mean SSIM metric, the solidarity filter provides results that are $11\,\%$ and $21\,\%$ closer to the ground truth than the corresponding results obtained by the median and close filters, respectively.