Depth Image Reconstruction Using Low Rank and Total Variation Representations

Abstract

Rapid advancement and active research in computer vision applications and 3D imaging have made a high demand for efficient depth image estimation techniques. The depth image acquisition, however, is typically challenged due to poor hardware performance and high computation cost. To tackle such limitations, this paper proposes an efficient approach for depth image reconstruction using low rank (LR) and total variation (TV) regularizations. The key idea is LR incorporates non-local depth information and TV takes into account the local spatial consistency. The proposed model reformulates the task of depth image estimate as a joint LR-TV regularized minimization problem, in which LR is used to approximate the low-dimensional structure of the depth image, and TV is employed to promote the depth sparsity in the gradient domain. Furthermore, this paper introduces an algorithm based on alternating direction method of multipliers (ADMM) for solving the minimization problem, whose solution provides an estimate of the depth map from incomplete pixels. Experimental results are conducted and the results show that the proposed approach is very effective at estimating high-quality depth images and is robust to different types of data missing models.