Depth upsampling method via Markov random fields without edge-misaligned artifacts

Recently, the widely use of time-of-flight sensors captures depth information for dynamic scenes in real time, which promotes the developing of many 3D image or video processing applications. However, such depth maps are noisy and have low resolutions. In this paper, we propose an edge-based depth map super-resolution method via solving a labeling optimization problem in MRF. The inputs are low quality depth map and the according high-resolution color image. The proposed method not only avoids the texture-copy artifacts, but also preserves the edges of depth which do not exist in the color image. We compare our algorithm with the state of the art on the benchmark dataset. The experimental results prove the validity and robustness of our approach.