A Tensor-based Enhancement Algorithm for Depth Video

In order to repair the dark holes in Kinect depth video, we propose a depth hole-filling method based on tensor.First, we process the original depth video by a weighted moving average system. Then, reconstruct the low-rank sensors and sparse sensors of the video utilize the tensor recovery method, through which the rough motion saliency can be initially separated from the background. Finally, construct a four-order tensor for moving target part, by grouping similar patches. Then we can formulate the video denoising and hole filling problem as a low-rank completion problem. In the proposed algorithm, the tensor model is used to preserve the spatial structure of the video modality. And we employ the block processing method to overcome the problem of information loss in traditional video processing based on frames. Experimental results show that our method can significantly improve the quality of depth video, and has strong robustness.