This paper presents a novel Time-of-Flight (ToF) depth denoising algorithm based on parametric noise modeling. ToF depth image includes space varying noise which is related to IR intensity value at each pixel. By assuming ToF depth noise as additive white Gaussian noise, ToF depth noise can be modeled by using a power function of IR intensity. Meanwhile, nonlocal means filter is popularly used as an edge-preserving denoising method for removing additive Gaussian noise. To remove space varying depth noise, we propose an adaptive nonlocal means filtering. According to the estimated noise, the search window and weighting coefficient are adaptively determined at each pixel so that pixels with large noise variance are strongly filtered and pixels with small noise variance are weakly filtered. Experimental results demonstrate that the proposed algorithm provides good denoising performance while preserving details or edges compared to the typical nonlocal means filtering.

Date Published: 30 January 2012
PDF: 8 pages
Proc. SPIE 8290, Three-Dimensional Image Processing (3DIP) and Applications II, 82900A (30 January 2012); doi: 10.1117/12.907614

Published in SPIE Proceedings Vol. 8290:
Three-Dimensional Image Processing (3DIP) and Applications II
Atilla M. Baskurt; Robert Sitnik, Editor(s)