Depth map super-resolution via low-resolution depth guided joint trilateral up-sampling

In this paper, a new method is proposed to address the depth map super resolution (SR) and denoising problems simultaneously. Unlike the existing methods, the proposed approach uses LR depth map as a guidance in each filtering iteration during the whole process to fully exploit the geometric information in it. A joint trilateral upsampling model is proposed to fuse the projected spatial distance measured from the LR depth map, the intensity variance extracted from the associated color image and the HR depth map generated in the last iteration to refine the HR depth map iteratively. Compared with the existing approaches, the proposed approach presents superior results in avoiding texture copying artifacts as misalignments existing between the depth map and color image. Also, for the depth with noises, it can provide stronger de-noising effects with much clearer edges in the processed results. On average, it only requires 7.67 iterations to reach convergence, which is very efficient and outperforms the representative approaches in terms of computational complexity, objective quality and subjective quality.