Depth enhancement with improved exemplar-based inpainting and joint trilateral guided filtering

Heavy noises and large amounts of holes exist in depth images captured by sensors, such as Kinect, which would severely hinder the application of depth information. In this paper, a novel depth enhancement algorithm with improved exemplar-based inpainting and joint trilateral guided filtering is proposed. The improved examplar-based inpainting method is applied to fill the holes in the depth images, in which the level set distance component is introduced in the priority evaluation function. Then a joint trilateral guided filter is adopted to denoise and smooth the inpainted results. Experimental results reveal that the proposed algorithm can achieve better enhancement results compared with the existing methods in terms of subjective and objective quality measurements.