Depth map inpainting via sparse distortion model

The depth map captured from a real scene by the Kinect motion sensor is always influenced by noise and other environmental factors. As a result, some depth information is missing from the map. This distortion of the depth map directly deteriorates the quality of the virtual viewpoints rendered in 3D video systems. We propose a depth map inpainting algorithm based on a sparse distortion model. First, we train the sparse distortion model using the distortion and real depth maps to obtain two learning dictionaries: one for distortion and one for real depth maps. Second, the sparse coefficients of the distortion and the real depth maps are calculated by orthogonal matching pursuit. We obtain the approximate features of the distortion from the relationship between the learning dictionary and the sparse coefficients of the distortion map. The noisy images are filtered by the joint space structure filter, and the extraction factor is obtained from the resulting image by the extraction factor judgment method. Finally, we combine the learning dictionary and sparse coefficients from the real depth map with the extraction factor to repair the distortion in the depth map. A quality evaluation method is proposed for the original real depth maps with missing pixels. The proposed method achieves better results than comparable methods in terms of depth inpainting and the subjective quality of the rendered virtual viewpoints.