Color Guided Depth Map Super-Resolution with Nonlocla Autoregres-Sive Modeling

Abstract

Depth map captured by 3D cameras usually suffers from low resolution and insufficient quality, which limits its applications in real world. Thus, it is an essential task to develop efficient and effective techniques to handle various depth degradations. In this paper, we propose a color guided depth map super-resolution method with nonlocal autoregressive modeling. Considering that textures in depth map demonstrate distinct geometry direction, we exploit the multi-directional dictionary which is effective in recovering subtle structures of depth patches. We further introduce two regularization terms into the sparse representation framework. Firstly, a patch based autoregressive model is introduced to represent the local patterns in a small area. Secondly, inspired by the structure consistence between depth map and color image, we propose a color guided nonlocal similarity to provide nonlocal constraint to the local structures, which is very helpful in preserving local structures and suppressing noise. Experimental results demonstrate the superior of our method compared with state-of-the-art methods.