An improved MRF model for robust color guided depth up-sampling

Color guided depth up-sampling always suffers from texture copy artifacts and depth discontinuities blurring. This phenomenon is due to the depth discontinuity and color image edges at the corresponding location are not always consistent. In this paper, based on the analysis of the above two problems, we proposed an improved Markov Random Filed (MRF) model, which can reduce negative influence from pixels at the inconsistent location more effectively. Furthermore, the improved MRF model can also reduce the negative influence of noises from depth map. To determine pixels of inconsistent regions, a new concept of pixel confidence is proposed. Pixel confidence indicates the probability that pixel is at the inconsistent regions, which is embedded into the improved MRF model. The proposed method is tested on both the simulated and real datasets. The proposed method can better suppress texture copy artifact and preserve sharp depth discontinuities. Experimental results show that the proposed method also has lower mean absolutely error(MAE) than other methods in most cases.