30 January 2012 Parametric model-based noise reduction for ToF depth sensors
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Abstract
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.
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Yong Sun Kim, Yong Sun Kim, Byongmin Kang, Byongmin Kang, Hwasup Lim, Hwasup Lim, Ouk Choi, Ouk Choi, Keechang Lee, Keechang Lee, James D. K. Kim, James D. K. Kim, Changyeong Kim, Changyeong Kim, } "Parametric model-based noise reduction for ToF depth sensors", Proc. SPIE 8290, Three-Dimensional Image Processing (3DIP) and Applications II, 82900A (30 January 2012); doi: 10.1117/12.907614; https://doi.org/10.1117/12.907614
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