Depth Restoration: A fast low-rank matrix completion via dual-graph regularization

TL;DR

This paper introduces a fast low-rank matrix completion method with dual-graph regularization for depth map restoration, effectively handling noise and missing data by leveraging local and non-local similarities.

Contribution

It proposes a novel dual-graph regularization approach that combines local and non-local information for efficient depth map restoration, with a closed-form solution for high speed.

Findings

Outperforms state-of-the-art methods in quality evaluations

Effectively restores depth maps with severe degeneration

Achieves high speed due to closed-form solution

Abstract

As a real scenes sensing approach, depth information obtains the widespread applications. However, resulting from the restriction of depth sensing technology, the depth map captured in practice usually suffers terrible noise and missing values at plenty of pixels. In this paper, we propose a fast low-rank matrix completion via dual-graph regularization for depth restoration. Specifically, the depth restoration can be transformed into a low-rank matrix completion problem. In order to complete the low-rank matrix and restore it to the depth map, the proposed dual-graph method containing the local and non-local graph regularizations exploits the local similarity of depth maps and the gradient consistency of depth-color counterparts respectively. In addition, the proposed approach achieves the high speed depth restoration due to closed-form solution. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods with respect to both objective and subjective quality evaluations, especially for serious depth degeneration.