Depth map super-resolution via local and nonlocal priors

Abstract

We propose a method for solving the depth map super-resolution problem. Given a low-resolution depth map as input, we recover a high-resolution depth map using a registered and potentially high-resolution color image. We formulate the super-resolution process as a regularization and optimization framework. Specifically, taking into account that discontinuities in depth and color tend to coalign, we obtain the local and nonlocal regularization terms based on the raw depth map and also the features of high-resolution color images. Different from conventional methods, we model the relationship with two constraint terms of local and nonlocal priors to sufficiently explore the complementary nature. Experimental results demonstrate the effectiveness of our approach, which produces sharper edges and more faithful details compared with other state-of-the-art strategies.